To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch c...To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.展开更多
The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant ...The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.展开更多
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw...In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.展开更多
The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A a...The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.展开更多
In this paper, a learning control approach is applied to the generalized projective synchronisation （GPS） of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov--Krasovski...In this paper, a learning control approach is applied to the generalized projective synchronisation （GPS） of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov--Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.展开更多
The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory...The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat＇s lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure.展开更多
A universal adaptive generalized functional synchronization approach to any two different or identical chaotic systems with unknown parameters is proposed, based on a unified mathematical expression of a large class o...A universal adaptive generalized functional synchronization approach to any two different or identical chaotic systems with unknown parameters is proposed, based on a unified mathematical expression of a large class of chaotic system. Self-adaptive parameter law and control law are given in the form of a theorem. The synchronization between the three-dimensional R6ssler chaotic system and the four-dimensional Chen＇s hyper-chaotic system is studied as an example for illustration. The computer simulation results demonstrate the feasibility of the method proposed.展开更多
A stabilized and convergent finite element formulation for the generalized Stokes problem is proposed and a posteriori analysis is performed to produce an error indicator. On this basis adaptive numerical method for s...A stabilized and convergent finite element formulation for the generalized Stokes problem is proposed and a posteriori analysis is performed to produce an error indicator. On this basis adaptive numerical method for solying the problem is developed . Numerical calculations are performed to confirm the reliability and effectiveness of the method.展开更多
Generalized Space Shift Keying （GSSK） modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and...Generalized Space Shift Keying （GSSK） modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and there exist redundant antenna combinations which are not used in GSSK. In this paper, a novel adaptive mapping scheme for GSSK modulation, named as Adaptive Mapping Generalized Space Shift Keying （AMGSSK）, is presented. Compared with GSSK, the antenna combinations are updated adaptively according to the Channel State Inforrmtion （CSI） in the proposed AMGSSK system, and the perfonrance of average Symbol Error Rate （SER） is reduced considerably. In the proposed scheme, two algorithrrs for selecting the optimum antenna combinations are described. The SER perfonmnce of AMGSSK is analyzed theoretically, and validated by Monte Carlo sinmlation. It is shown that the proposed AMGSSK scheme has good perfonmnce in SER and spectral efficiency.展开更多
The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimiz...The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.展开更多
A complete mesh free adaptive algorithm （MFAA）, with solution adaptation and geometric adaptation, is developed to improve the resolution of flow features and to replace traditional global refinement techniques in s...A complete mesh free adaptive algorithm （MFAA）, with solution adaptation and geometric adaptation, is developed to improve the resolution of flow features and to replace traditional global refinement techniques in structured grids. Unnecessary redundant points and elements are avoided by using the mesh free local clouds refinement technology in shock influencing regions and regions near large curvature places on the boundary. Inviscid compressible flows over NACA0012 and RAE2822 airfoils are computed. Finally numerical results validate the accuracy of the above method.展开更多
On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in t...On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.展开更多
A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass ...A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.展开更多
Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective funct...Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.展开更多
An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, ...An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, the convergent speed of the on-line control path identification process is significantly improved. As a result, the controller performance is greatly enhanced. Besides the algorithm development, some important factors, such as the influence of reference signal on the controller convergent speed, are also discussed. The effectiveness of the algorithm is verified by experimental results.展开更多
We propose a slope-based decoupling algorithm to simultaneously control the dual deformable mirrors （DMs） in a woofer-tweeter adaptive optics system. This algorithm can directly use the woofer＇s response matrix mea...We propose a slope-based decoupling algorithm to simultaneously control the dual deformable mirrors （DMs） in a woofer-tweeter adaptive optics system. This algorithm can directly use the woofer＇s response matrix measured from a Shack-Hartmann wave-front sensor to construct a slope-based orthogonal basis, and then selectively distribute the large- amplitude low-order aberration to woofer DM and the remaining aberration to tweeter DM through the slope-based orthogonal basis. At the same moment, in order to avoid the two DMs generating opposite compensation, a constraint matrix used to reset tweeter control vector is convenient to be calculated with the slope-based orthogonal basis. Numeral simulation demonstrates that this algorithm has a good performance to control the adaptive optics system with dual DMs simultaneously. Compared with the typical decoupling algorithm, this algorithm can take full use of the compensation ability of woofer DM and release the stroke of tweeter DM to compensate high-order aberration. More importantly, it does not need to measure the accurate shape of tweeter＇s influence function and keeps better performance of restraining the coupling error with the continuous-dynamic aberration.展开更多
An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach....An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice.展开更多
We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping...We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping is continuous and f-pseudomonotone with nonempty compact convex values on dom（f）, where f ： Rn --RU{＋∞} is a proper func- tion. The algorithm presented in this paper generalize and improve some known algorithms in literatures. Preliminary computational experience is also reported.展开更多
An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal ...An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.展开更多
The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in mic...The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array(FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge(SOC)equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.展开更多
文摘To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.
基金supported by the 2021 Open Project Fund of Science and Technology on Electromechanical Dynamic Control Laboratory,grant number 212-C-J-F-QT-2022-0020China Postdoctoral Science Foundation,grant number 2021M701713+1 种基金Postgraduate Research&Practice Innovation Program of Jiangsu Province,grant number KYCX23_0511the Jiangsu Funding Program for Excellent Postdoctoral Talent,grant number 20220ZB245。
文摘The phenomenon of a target echo peak overlapping with the backscattered echo peak significantly undermines the detection range and precision of underwater laser fuzes.To overcome this issue,we propose a four-quadrant dual-beam circumferential scanning laser fuze to distinguish various interference signals and provide more real-time data for the backscatter filtering algorithm.This enhances the algorithm loading capability of the fuze.In order to address the problem of insufficient filtering capacity in existing linear backscatter filtering algorithms,we develop a nonlinear backscattering adaptive filter based on the spline adaptive filter least mean square(SAF-LMS)algorithm.We also designed an algorithm pause module to retain the original trend of the target echo peak,improving the time discrimination accuracy and anti-interference capability of the fuze.Finally,experiments are conducted with varying signal-to-noise ratios of the original underwater target echo signals.The experimental results show that the average signal-to-noise ratio before and after filtering can be improved by more than31 d B,with an increase of up to 76%in extreme detection distance.
文摘In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.
基金supported by the National Natural Science Foundation of China(Nos.62265010,62061024)Gansu Province Science and Technology Plan(No.23YFGA0062)Gansu Province Innovation Fund(No.2022A-215)。
文摘The paper proposes a wireless sensor network(WSN)localization algorithm based on adaptive whale neural network and extended Kalman filtering to address the problem of excessive reliance on environmental parameters A and signal constant n in traditional signal propagation path loss models.This algorithm utilizes the adaptive whale optimization algorithm to iteratively optimize the parameters of the backpropagation(BP)neural network,thereby enhancing its prediction performance.To address the issue of low accuracy and large errors in traditional received signal strength indication(RSSI),the algorithm first uses the extended Kalman filtering model to smooth the RSSI signal values to suppress the influence of noise and outliers on the estimation results.The processed RSSI values are used as inputs to the neural network,with distance values as outputs,resulting in more accurate ranging results.Finally,the position of the node to be measured is determined by combining the weighted centroid algorithm.Experimental simulation results show that compared to the standard centroid algorithm,weighted centroid algorithm,BP weighted centroid algorithm,and whale optimization algorithm(WOA)-BP weighted centroid algorithm,the proposed algorithm reduces the average localization error by 58.23%,42.71%,31.89%,and 17.57%,respectively,validating the effectiveness and superiority of the algorithm.
基金supported by the National Natural Science Foundation of China (Grant No. 60374015)
文摘In this paper, a learning control approach is applied to the generalized projective synchronisation （GPS） of different chaotic systems with unknown periodically time-varying parameters. Using the Lyapunov--Krasovskii functional stability theory, a differential-difference mixed parametric learning law and an adaptive learning control law are constructed to make the states of two different chaotic systems asymptotically synchronised. The scheme is successfully applied to the generalized projective synchronisation between the Lorenz system and Chen system. Moreover, numerical simulations results are used to verify the effectiveness of the proposed scheme.
文摘The adaptive generalized matrix projective lag synchronization between two different complex networks with non-identical nodes and different dimensions is investigated in this paper. Based on Lyapunov stability theory and Barbalat＇s lemma, generalized matrix projective lag synchronization criteria are derived by using the adaptive control method. Furthermore, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. In addition, numerical simulation results are presented to illustrate the effectiveness of this method, showing that the synchronization speed is sensitively influenced by the adaptive law strength, the network size, and the network topological structure.
基金Project supported by the National Natural Science Foundation of China (Grant No 50677021)partially by the Key Project Foundation of North China Electric Power University (Grant No 20041306)by the Scientific Research Foundation for the Returned Overseas Chinese Scholar, NCEPU (Grant No 200814002)
文摘A universal adaptive generalized functional synchronization approach to any two different or identical chaotic systems with unknown parameters is proposed, based on a unified mathematical expression of a large class of chaotic system. Self-adaptive parameter law and control law are given in the form of a theorem. The synchronization between the three-dimensional R6ssler chaotic system and the four-dimensional Chen＇s hyper-chaotic system is studied as an example for illustration. The computer simulation results demonstrate the feasibility of the method proposed.
文摘A stabilized and convergent finite element formulation for the generalized Stokes problem is proposed and a posteriori analysis is performed to produce an error indicator. On this basis adaptive numerical method for solying the problem is developed . Numerical calculations are performed to confirm the reliability and effectiveness of the method.
基金supported partially by the National Key Basic Research Program of China under Grant No.2007CB310605the Science and Technology Development Fund of Tianjin Colleges and Universities under Grant No.20080708the Research Fund of Tianjin University of Technology and Education under Grants No.KJ09-012,No.KJ10-06
文摘Generalized Space Shift Keying （GSSK） modulation is a low-complexity spatial nmltiplexing technique for nmltiple-antenna wireless systems. However, effective transmit antenna combinations have to be preselected, and there exist redundant antenna combinations which are not used in GSSK. In this paper, a novel adaptive mapping scheme for GSSK modulation, named as Adaptive Mapping Generalized Space Shift Keying （AMGSSK）, is presented. Compared with GSSK, the antenna combinations are updated adaptively according to the Channel State Inforrmtion （CSI） in the proposed AMGSSK system, and the perfonrance of average Symbol Error Rate （SER） is reduced considerably. In the proposed scheme, two algorithrrs for selecting the optimum antenna combinations are described. The SER perfonmnce of AMGSSK is analyzed theoretically, and validated by Monte Carlo sinmlation. It is shown that the proposed AMGSSK scheme has good perfonmnce in SER and spectral efficiency.
文摘The performance of genetic algorithm(GA) is determined by the capability of search and optimization for satisfactory solutions. The new adaptive genetic algorithm(AGA) is built for inducing suitable search and optimization relationship. The use of six fuzzy logic controllers(6FLCs) is proposed for dynamic control genetic operating parameters of a symbolic-coded GA. This paper uses AGA based on 6FLCs to deal with the travelling salesman problem (TSP). Experimental results show that AGA based on 6FLCs is more efficient than a standard GA in solving combinatorial optimization problems similar to TSP.
文摘A complete mesh free adaptive algorithm （MFAA）, with solution adaptation and geometric adaptation, is developed to improve the resolution of flow features and to replace traditional global refinement techniques in structured grids. Unnecessary redundant points and elements are avoided by using the mesh free local clouds refinement technology in shock influencing regions and regions near large curvature places on the boundary. Inviscid compressible flows over NACA0012 and RAE2822 airfoils are computed. Finally numerical results validate the accuracy of the above method.
文摘On the basis of the theory of adaptive active noise control(AANC) in a duct, this article discusses the algorithms of the adaptive control, compares the algorithm characteristics using LMS, RLS and LSL algorithms in the adaptive filter in the AANC system, derives the recursive formulas of LMS algorithm. and obtains the LMS algorithm in computer simulation using FIR and IIR filters in AANC system. By means of simulation, we compare the attenuation levels with various input signals in AANC system and discuss the effects of step factor, order of filters and sound delay on the algorithm's convergence rate and attenuation level.We also discuss the attenuation levels with sound feedback using are and IIR filters in AANC system.
文摘A new algorithm, called the adaptive exponent smoothing gradient algorithm (AESGA), is developed from Widrow′s LMS algorithm. It is based on the fact that LMS algorithm has properties of time delaying and low pass filtering. This paper shows that the algorithm, on the domain of {Ω 1:α∈(0,1)}×{Ω 2:β(0,∞)} , unbiasedly and asymptotically converges to the Winner solution when the signal is a stationary Gauss stochastic process. The convergent property and the performance misadjustment are analyzed in theory. And calculation method of the algorithm is also suggested. Numerical results given by computer simulations show that the algorithm is effective.
基金supported by the the Youth Science and Technology Innovation Fund (Science)(Nos.NS2014070, NS2014070)
文摘Air route network(ARN)planning is an efficient way to alleviate civil aviation flight delays caused by increasing development and pressure for safe operation.Here,the ARN shortest path was taken as the objective function,and an air route network node(ARNN)optimization model was developed to circumvent the restrictions imposed by″three areas″,also known as prohibited areas,restricted areas,and dangerous areas(PRDs),by creating agrid environment.And finally the objective function was solved by means of an adaptive ant colony algorithm(AACA).The A593,A470,B221,and G204 air routes in the busy ZSHA flight information region,where the airspace includes areas with different levels of PRDs,were taken as an example.Based on current flight patterns,a layout optimization of the ARNN was computed using this model and algorithm and successfully avoided PRDs.The optimized result reduced the total length of routes by 2.14% and the total cost by 9.875%.
文摘An improved algorithm is presented to identify the secondary path based on the adaptive notch filter approach. Since the interference from the narrow band excitation signal is suppressed by the adaptive notch filter, the convergent speed of the on-line control path identification process is significantly improved. As a result, the controller performance is greatly enhanced. Besides the algorithm development, some important factors, such as the influence of reference signal on the controller convergent speed, are also discussed. The effectiveness of the algorithm is verified by experimental results.
基金Project supported by the Key Scientific Equipment Development Project of China(Grant No.ZDYZ2013-2)the National High-Tech R&D Program of China(Grant Nos.G128201-G158201 and G128603-G158603)+2 种基金the Innovation Fund of Chinese Academy of Science(Grant No.CXJJ-16M208)the Youth Innovation Promotion Association of the Chinese Academy of Sciencesthe Outstanding Young Scientists,Chinese Academy of Sciences
文摘We propose a slope-based decoupling algorithm to simultaneously control the dual deformable mirrors （DMs） in a woofer-tweeter adaptive optics system. This algorithm can directly use the woofer＇s response matrix measured from a Shack-Hartmann wave-front sensor to construct a slope-based orthogonal basis, and then selectively distribute the large- amplitude low-order aberration to woofer DM and the remaining aberration to tweeter DM through the slope-based orthogonal basis. At the same moment, in order to avoid the two DMs generating opposite compensation, a constraint matrix used to reset tweeter control vector is convenient to be calculated with the slope-based orthogonal basis. Numeral simulation demonstrates that this algorithm has a good performance to control the adaptive optics system with dual DMs simultaneously. Compared with the typical decoupling algorithm, this algorithm can take full use of the compensation ability of woofer DM and release the stroke of tweeter DM to compensate high-order aberration. More importantly, it does not need to measure the accurate shape of tweeter＇s influence function and keeps better performance of restraining the coupling error with the continuous-dynamic aberration.
文摘An improved adaptive genetic algorithm is presented in this paper. It primarily includes two modified methods： one is novel adaptive probabilities of crossover and mutation, the other is truncated selection approach. This algorithm has been validated to be superior to the simple genetic algorithm （SGA） by a complicated binary testing function. Then the proposed algorithm is applied to optimizing the planar retrodirective array to reduce the cost of the hardware. The fitness function is discussed in the optimization example. After optimization, the sparse planar retrodirective antenna array keeps excellent retrodirectivity, while the array architecture has been simplified by 34%. The optimized antenna array can replace uniform full array effectively. Results show that this work will gain more engineering benefits in practice.
基金supported by the Scientific Research Foundation of Sichuan Normal University(20151602)National Natural Science Foundation of China(10671135,61179033)and the Key Project of Chinese Ministry of Education(212147)
文摘We propose a projection-type algorithm for generalized mixed variational in- equality problem in Euclidean space Rn. We establish the convergence theorem for the pro- posed algorithm, provided the multi-valued mapping is continuous and f-pseudomonotone with nonempty compact convex values on dom（f）, where f ： Rn --RU{＋∞} is a proper func- tion. The algorithm presented in this paper generalize and improve some known algorithms in literatures. Preliminary computational experience is also reported.
基金Supported by the National Natural Science Foundation of China(51175262)the Research Fund for Doctoral Program of Higher Education of China(20093218110020)+2 种基金the Jiangsu Province Science Foundation for Excellent Youths(BK201210111)the Jiangsu Province Industry-Academy-Research Grant(BY201220116)the Innovative and Excellent Foundation for Doctoral Dissertation of Nanjing University of Aeronautics and Astronautics(BCXJ10-09)
文摘An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.
基金National Natural Science Foundation of China(51977160)“Voltage Self balancing Control Method for Modular Multilevel Converter Based on Switching State Matrix”.
文摘The application of virtual synchronous generator(VSG)control in flywheel energy storage systems(FESS)is an effective solution for addressing the challenges related to reduced inertia and inadequate power supply in microgrids.Considering the significant variations among individual units within a flywheel array and the poor frequency regulation performance under conventional control approaches,this paper proposes an adaptive VSG control strategy for a flywheel energy storage array(FESA).First,by leveraging the FESA model,a variable acceleration factor is integrated into the speed-balance control strategy to effectively achieve better state of charge(SOC)equalization across units.Furthermore,energy control with a dead zone is introduced to prevent SOC of the FESA from exceeding the limit.The dead zone parameter is designed based on the SOC warning intervals of the flywheel array to mitigate its impact on regular operation.In addition,VSG technology is applied for the grid-connected control of the FESA,and the damping characteristic of the VSG is decoupled from the primary frequency regulation through power differential feedback.This ensures optimal dynamic performance while reducing the need for frequent involvement in frequency regulation.Subsequently,a parameter design method is developed through a small-signal stability analysis.Consequently,considering the SOC of the FESA,an adaptive control strategy for the inertia damping and the P/ωdroop coefficient of the VSG control is proposed to optimize the grid support services of the FESA.Finally,the effectiveness of the proposed control methods is demonstrated through electromagnetic transient simulations using MATLAB/Simulink.
