Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types o...Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.展开更多
Gear assembly errors can lead to the increase of vibration and noise of the system,which affect the stability of system.The influence can be compensated by tooth modification.Firstly,an improved three-dimensional load...Gear assembly errors can lead to the increase of vibration and noise of the system,which affect the stability of system.The influence can be compensated by tooth modification.Firstly,an improved three-dimensional loaded tooth contact analysis(3D-LTCA)method which can consider tooth modification and coupling assembly errors is proposed,and mesh stiffness calculated by proposed method is verified by MASTA software.Secondly,based on neural network,the surrogate model(SM)that maps the relationship between modification parameters and mesh mechanical parameters is established,and its accuracy is verified.Finally,SM is introduced to establish an optimization model with the target of minimizing mesh stiffness variations and obtaining more even load distribution on mesh surface.The results show that even considering training time,the efficiency of gear pair optimization by surrogate model is still much higher than that by LTCA method.After optimization,the mesh stiffness fluctuation of gear pair with coupling assembly error is reduced by 34.10%,and difference in average contact stresses between left and right regions of the mesh surface is reduced by 62.84%.展开更多
Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, networ...Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regio...This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.展开更多
In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures ...In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures contained inner and outer tapered tubes,and four stiffening plates connected them together.The parameter a/b corresponds to the inner tube side length to the outer tube one.In addition,the space between the inner and outer tubes was filled with polyurethane foam.After validating the finite element model generated in LS-DYNA using the results of experimental tests,crashworthiness indicators of SEA(specific energy absorption)and Fmax(peak crushing force)were obtained for the studied structures.Based on the TOPSIS calculations,the semi-foam filled decagonal structure with the ratio of a/b=0.5 demonstrated the best crashworthiness capability among the studied ratios of a/b.Finally,optimum thicknesses(t1(thickness of the outer tube),t2(thickness of the inner tube),t3(thickness of the stiffening plates))of the selected decagonal structure were obtained by adopting RBF(radial basis function)neural network and genetic algorithm.展开更多
An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learnin...An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.展开更多
A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of c...A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method.High precise and robust tracking of required attitude angles can be achieved in complicated air space.A mathematical model of reusable launch vehicle is presented first,and then a controller of flight system is presented.Base on the mathematical model,the controller is divided into two parts:variable-structure controller and neural network module which is used to modify the parameters of controller.This control system decouples the lateraldirectional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles.After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters.The final simulation shows that the control system has a good accurate,robust and de-coupled tracking performance.The stable state error is less than 1°,and the overshoot is less than 5%.展开更多
This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtai...This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.展开更多
Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorith...Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
This paper deals with fault isolation in nonlinear analog circuits with tolerance under an insufficient number of independent voltage measurements.A neural network-based L1-norm optimization approach is proposed and u...This paper deals with fault isolation in nonlinear analog circuits with tolerance under an insufficient number of independent voltage measurements.A neural network-based L1-norm optimization approach is proposed and utilized in locating the most likely faulty elements in nonlinear circuits.The validity of the proposed method is verified by both extensive computer simulations and practical examples.One simulation example is presented in the paper.展开更多
Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony opt...Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.展开更多
Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a ta...Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.展开更多
An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amp...An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved, and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass, bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely .The presented optimal design approach of high order FIR digital filter is significantly effective.展开更多
Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the dat...Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.展开更多
This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network,...This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.展开更多
文摘Fault diagnosis occupies a pivotal position within the domain of machine and equipment management.Existing methods,however,often exhibit limitations in their scope of application,typically focusing on specific types of signals or faults in individual mechanical components while being constrained by data types and inherent characteristics.To address the limitations of existing methods,we propose a fault diagnosis method based on graph neural networks(GNNs)embedded with multirelationships of intrinsic mode functions(MIMF).The approach introduces a novel graph topological structure constructed from the features of intrinsic mode functions(IMFs)of monitored signals and their multirelationships.Additionally,a graph-level based fault diagnosis network model is designed to enhance feature learning capabilities for graph samples and enable flexible application across diverse signal sources and devices.Experimental validation with datasets including independent vibration signals for gear fault detection,mixed vibration signals for concurrent gear and bearing faults,and pressure signals for hydraulic cylinder leakage characterization demonstrates the model's adaptability and superior diagnostic accuracy across various types of signals and mechanical systems.
基金Project(11972112)supported by the National Natural Science Foundation of ChinaProject(N2103024)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(J2019-IV-0018-0086)supported by the National Science and Technology Major Project,China。
文摘Gear assembly errors can lead to the increase of vibration and noise of the system,which affect the stability of system.The influence can be compensated by tooth modification.Firstly,an improved three-dimensional loaded tooth contact analysis(3D-LTCA)method which can consider tooth modification and coupling assembly errors is proposed,and mesh stiffness calculated by proposed method is verified by MASTA software.Secondly,based on neural network,the surrogate model(SM)that maps the relationship between modification parameters and mesh mechanical parameters is established,and its accuracy is verified.Finally,SM is introduced to establish an optimization model with the target of minimizing mesh stiffness variations and obtaining more even load distribution on mesh surface.The results show that even considering training time,the efficiency of gear pair optimization by surrogate model is still much higher than that by LTCA method.After optimization,the mesh stiffness fluctuation of gear pair with coupling assembly error is reduced by 34.10%,and difference in average contact stresses between left and right regions of the mesh surface is reduced by 62.84%.
基金Projects(70373017 70572090) supported by the National Natural Science Foundation of China
文摘Transmission network planning (TNP) is a large-scale, complex, with more non-linear discrete variables and the multi-objective constrained optimization problem. In the optimization process, the line investment, network reliability and the network loss are the main objective of transmission network planning. Combined with set pair analysis (SPA), particle swarm optimization (PSO), neural network (NN), a hybrid particle swarm optimization model was established with neural network and set pair analysis for transmission network planning (HPNS). Firstly, the contact degree of set pair analysis was introduced, the traditional goal set was converted into the collection of the three indicators including the identity degree, difference agree and contrary degree. On this bases, using shi(H), the three objective optimization problem was converted into single objective optimization problem. Secondly, using the fast and efficient search capabilities of PSO, the transmission network planning model based on set pair analysis was optimized. In the process of optimization, by improving the BP neural network constantly training so that the value of the fitness function of PSO becomes smaller in order to obtain the optimization program fitting the three objectives better. Finally, compared HPNS with PSO algorithm and the classic genetic algorithm, HPNS increased about 23% efficiency than THA, raised about 3.7% than PSO and improved about 2.96% than GA.
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
基金supported by the National Natural Science Foundation of China(61233010 61305106)+2 种基金the Shanghai Natural Science Foundation(17ZR1409700 18ZR1415300)the basic research project of Shanghai Municipal Science and Technology Commission(16JC1400900)
文摘This paper concerns the problem of object segmentation in real-time for picking system. A region proposal method inspired by human glance based on the convolutional neural network is proposed to select promising regions, allowing more processing is reserved only for these regions. The speed of object segmentation is significantly improved by the region proposal method.By the combination of the region proposal method based on the convolutional neural network and superpixel method, the category and location information can be used to segment objects and image redundancy is significantly reduced. The processing time is reduced considerably by this to achieve the real time. Experiments show that the proposed method can segment the interested target object in real time on an ordinary laptop.
基金Project(1365-96/7/22) supported by University of Mohaghegh Ardabili,Iran
文摘In this research,crashworthiness of polyurethane foam-filled tapered decagonal structures with different ratios of a/b=0,0.25,0.5,0.75 and 1 was evaluated under axial and oblique impacts.These new designed structures contained inner and outer tapered tubes,and four stiffening plates connected them together.The parameter a/b corresponds to the inner tube side length to the outer tube one.In addition,the space between the inner and outer tubes was filled with polyurethane foam.After validating the finite element model generated in LS-DYNA using the results of experimental tests,crashworthiness indicators of SEA(specific energy absorption)and Fmax(peak crushing force)were obtained for the studied structures.Based on the TOPSIS calculations,the semi-foam filled decagonal structure with the ratio of a/b=0.5 demonstrated the best crashworthiness capability among the studied ratios of a/b.Finally,optimum thicknesses(t1(thickness of the outer tube),t2(thickness of the inner tube),t3(thickness of the stiffening plates))of the selected decagonal structure were obtained by adopting RBF(radial basis function)neural network and genetic algorithm.
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘An improved wavelet neural network algorithm which combines with particle swarm optimization was proposed to avoid encountering the curse of dimensionality and overcome the shortage in the responding speed and learning ability brought about by the traditional models. Based on the operational data provided by a regional power grid in the south of China, the method was used in the actual short term load forecasting. The results show that the average time cost of the proposed method in the experiment process is reduced by 12.2 s, and the precision of the proposed method is increased by 3.43% compared to the traditional wavelet network. Consequently, the improved wavelet neural network forecasting model is better than the traditional wavelet neural network forecasting model in both forecasting effect and network function.
文摘A flight control system is designed for a reusable launch vehicle with aerodynamic control surfaces and reaction control system based on a variable-structure control and neural network theory.The control problems of coupling among the channels and the uncertainty of model parameters are solved by using the method.High precise and robust tracking of required attitude angles can be achieved in complicated air space.A mathematical model of reusable launch vehicle is presented first,and then a controller of flight system is presented.Base on the mathematical model,the controller is divided into two parts:variable-structure controller and neural network module which is used to modify the parameters of controller.This control system decouples the lateraldirectional tunnels well with a neural network sliding mode controller and provides a robust and de-coupled tracking for mission angle profiles.After this a control allocation algorithm is employed to allocate the torque moments to aerodynamic control surfaces and thrusters.The final simulation shows that the control system has a good accurate,robust and de-coupled tracking performance.The stable state error is less than 1°,and the overshoot is less than 5%.
基金supported by the National Natural Science Foundation of China(61273190)
文摘This paper proposes an optimization algorithm based on a multi-loop control system with a neural network controller,in which the objective function that is used is the control plant of each sub-control system.To obtain the global optimization solution from a control plant that has many local minimum points,a transformation function is presented.On the one hand,this approach changes a complex objective function into a simple function under the condition of an unchanged globally optimal solution,to find the global optimization solution more easily by using a multi-loop control system.On the other hand,a special neural network(in which the node function can be simply positioned locally)that is composed of multiple transformation functions is used as the controller,which reduces the possibility of falling into local minimum points.At the same time,a filled function is presented as a control law;it can jump out of a local minimum point and move to another local minimum point that has a smaller value of the objective function.Finally,18 simulation examples are provided to show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(7110111671271170)+1 种基金the Program for New Century Excellent Talents in University(NCET-13-0475)the Basic Research Foundation of NPU(JC20120228)
文摘Finding out reasonable structures from bulky data is one of the difficulties in modeling of Bayesian network （BN）, which is also necessary in promoting the application of BN. This pa- per proposes an immune algorithm based method （BN-IA） for the learning of the BN structure with the idea of vaccination. Further- more, the methods on how to extract the effective vaccines from local optimal structure and root nodes are also described in details. Finally, the simulation studies are implemented with the helicopter convertor BN model and the car start BN model. The comparison results show that the proposed vaccines and the BN-IA can learn the BN structure effectively and efficiently.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
文摘This paper deals with fault isolation in nonlinear analog circuits with tolerance under an insufficient number of independent voltage measurements.A neural network-based L1-norm optimization approach is proposed and utilized in locating the most likely faulty elements in nonlinear circuits.The validity of the proposed method is verified by both extensive computer simulations and practical examples.One simulation example is presented in the paper.
基金supported by the National Natural Science Foundation of China (60974082,11171094)the Fundamental Research Funds for the Central Universities (K50510700004)+1 种基金the Foundation and Advanced Technology Research Program of Henan Province (102300410264)the Basic Research Program of the Education Department of Henan Province (2010A110010)
文摘Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization（U-ACO-B） to solve the drawbacks of the ant colony optimization（ACO-B）.In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.
文摘Target maneuver trajectory prediction plays an important role in air combat situation awareness and threat assessment.To solve the problem of low prediction accuracy of the traditional prediction method and model,a target maneuver trajectory prediction model based on phase space reconstruction-radial basis function(PSR-RBF)neural network is established by combining the characteristics of trajectory with time continuity.In order to further improve the prediction performance of the model,the rival penalized competitive learning(RPCL)algorithm is introduced to determine the structure of RBF,the Levenberg-Marquardt(LM)and the hybrid algorithm of the improved particle swarm optimization(IPSO)algorithm and the k-means are introduced to optimize the parameter of RBF,and a PSR-RBF neural network is constructed.An independent method of 3D coordinates of the target maneuver trajectory is proposed,and the target manuver trajectory sample data is constructed by using the training data selected in the air combat maneuver instrument(ACMI),and the maneuver trajectory prediction model based on the PSR-RBF neural network is established.In order to verify the precision and real-time performance of the trajectory prediction model,the simulation experiment of target maneuver trajectory is performed.The results show that the prediction performance of the independent method is better,and the accuracy of the PSR-RBF prediction model proposed is better.The prediction confirms the effectiveness and applicability of the proposed method and model.
基金This project was supported by the National Natural Science Foundation of China (50277010)Doctoral Special Fund of Ministry of Education (20020532016) and Fund of Outstanding Young Scientist of Hunan University.
文摘An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved, and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass, bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely .The presented optimal design approach of high order FIR digital filter is significantly effective.
基金Project(70373017) supported by the National Natural Science Foundation of China
文摘Considering multi-factor influence, a forecasting model was built. The structure of BP neural network was designed, and immune algorithm was applied to optimize its network structure and weight. After training the data of power demand from the year 1980 to 2005 in China, a nonlinear network model was obtained on the relationship between power demand and the factors which had impacts on it, and thus the above proposed method was verified. Meanwhile, the results were compared to those of neural network optimized by genetic algorithm. The results show that this method is superior to neural network optimized by genetic algorithm and is one of the effective ways of time series forecast.
基金Supported by National Natural Science Foundation of China （60496322）, Natural Science Foundation of Beijing （4083034）, and Scientific Research Common Program of Beijing Municipal Commission.of Education （KM200610005020）_ _ _
基金Supported by UK EPSRC (grants GR/N13319 and GR/R 10875)
文摘This paper presents several neural network based modelling, reliable optimal control, and iterative learning control methods for batch processes. In order to overcome the lack of robustness of a single neural network, bootstrap aggregated neural networks are used to build reliable data based empirical models. Apart from improving the model generalisation capability, a bootstrap aggregated neural network can also provide model prediction confidence bounds. A reliable optimal control method by incorporating model prediction confidence bounds into the optimisation objective function is presented. A neural network based iterative learning control strategy is presented to overcome the problem due to unknown disturbances and model-plant mismatches. The proposed methods are demonstrated on a simulated batch polymerisation process.
