We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution co...We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution converges weakly to the law of a stochastic evolution equation with an additive Gaussian process.展开更多
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(...In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.展开更多
As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configu...As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.展开更多
In the conceptual design stage of complex products, CBR（Case-Based Reasoning） tool is useful to offer a feasible set of schemes. Then the most adaptive scheme can be generated through a procedure of comparison and e...In the conceptual design stage of complex products, CBR（Case-Based Reasoning） tool is useful to offer a feasible set of schemes. Then the most adaptive scheme can be generated through a procedure of comparison and evaluation. The procedure is essentially a multiple criteria decision-making problem. The traditional multiple criteria programming is not flexible enough in executing the system evaluation algorithm due to both the limited experimental data and the lack of human experiences. To make the CBR tool to be more efficient, a new method for the best choice among the feasible schemes based on the Fuzzy AHP using Fuzzy numbers （FFAHP） is proposed. Since the final results become a problem of ranking the mean of fuzzy numbers by the optimism of decision-maker using the FFAHP, its execution is much more intuitive and effective than with the traditional method.展开更多
The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is...The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.展开更多
The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard...The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.展开更多
Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors...Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.展开更多
The power transformer is the key equipment of transforming voltage and exchanging power in the power system.It's safe and reliable operation directly influences the safe level of the power system.To study the risk...The power transformer is the key equipment of transforming voltage and exchanging power in the power system.It's safe and reliable operation directly influences the safe level of the power system.To study the risk assessment of power transformer which is very significant to improve the reliability of the power system,a fuzzy comprehensive risk assessment model of power transformer based on Borda number theory is proposed in this paper.At first,the fault types and risk factors of the power transformer are analyzed.Secondly,the basic framework of the fuzzy comprehensive evaluation is applied to quantify the risk factors.And then,Borda number theory is employed to analyze influence degree and occurrence probability of power transformer.At last,the various risk factors impact index and fuzzy comprehensive evaluation index of power transformer can be easily obtained.Applying this model,the relative importance degree of the risk factors can be horizontally compared according to the numerical index,the engineering staff can directly get the parameters of the transformer risk level and get a good description of the visual expression through using 5 score and similar visual language.展开更多
Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this ...Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.展开更多
This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is va...This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is validated from soft tissue cutting. Then, based on the acquired input-output data pairs, a method for fuzzy system modeling is presented, that is, after partitioning each input space equally and giving the premises and the total number of fuzzy rules, the consequent parameters and the fuzzy membership functions (MF) of the input variables are learned and optimized via a neurofuzzy modeling technique. Finally, a haptic scalpel implemented with the established cutting model is described. Preliminary results show the feasibility of the haptic display system for real-time interaction.展开更多
基金Supported by the Science and Technology Research Projects of Hubei Provincial Department of Education(B2022077)。
文摘We study the distribution limit of a class of stochastic evolution equation driven by an additive-stable Non-Gaussian process in the case of α∈(1,2).We prove that,under suitable conditions,the law of the solution converges weakly to the law of a stochastic evolution equation with an additive Gaussian process.
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
基金Project(50977003) supported by the National Natural Science Foundation of China
文摘In operation,risk arising from power transformer faults is of much uncertainty and complicacy.To timely and objectively control the risks,a transformer risk assessment method based on fuzzy analytic hierarchy process(FAHP) and artificial neural network(ANN) from the perspective of accuracy and quickness is proposed.An analytic hierarchy process model for the transformer risk assessment is built by analysis of the risk factors affecting the transformer risk level and the weight relation of each risk factor in transformer risk calculation is analyzed by application of fuzzy consistency judgment matrix;with utilization of adaptive ability and nonlinear mapping ability of the ANN,the risk factors with large weights are used as input of neutral network,and thus intelligent quantitative assessment of transformer risk is realized.The simulation result shows that the proposed method increases the speed and accuracy of the risk assessment and can provide feasible decision basis for the transformer risk management and maintenance decisions.
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘As a production quality index of hematite grinding process,particle size(PS)is hard to be measured in real time.To achieve the PS estimation,this paper proposes a novel data driven model of PS using stochastic configuration network(SCN)with robust technique,namely,robust SCN(RSCN).Firstly,this paper proves the universal approximation property of RSCN with weighted least squares technique.Secondly,three robust algorithms are presented by employing M-estimation with Huber loss function,M-estimation with interquartile range(IQR)and nonparametric kernel density estimation(NKDE)function respectively to set the penalty weight.Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods,and then the data-driven PS model based on the robust algorithms are established and verified.Experimental results show that the RSCN has an excellent performance for the PS estimation.
基金This project was partly supported bythe Key Programof the National Natural Science Foundation of China (79990580) .
文摘In the conceptual design stage of complex products, CBR（Case-Based Reasoning） tool is useful to offer a feasible set of schemes. Then the most adaptive scheme can be generated through a procedure of comparison and evaluation. The procedure is essentially a multiple criteria decision-making problem. The traditional multiple criteria programming is not flexible enough in executing the system evaluation algorithm due to both the limited experimental data and the lack of human experiences. To make the CBR tool to be more efficient, a new method for the best choice among the feasible schemes based on the Fuzzy AHP using Fuzzy numbers （FFAHP） is proposed. Since the final results become a problem of ranking the mean of fuzzy numbers by the optimism of decision-maker using the FFAHP, its execution is much more intuitive and effective than with the traditional method.
基金Supported by National Natural Science Foundation of P. R. China (60572070, 60325311, 60534010) Natural Science Foundation of Liaoning Province (20022030)
文摘The problem of track control is studied for a class of strict-feedback stochastic nonlinear systems in which unknown virtual control gain function is the main feature. First, the so-called stochastic LaSalle theory is extended to some extent, and accordingly, the results of global ultimate boundedness for stochastic nonlinear systems are developed. Next, a new design scheme of fuzzy adaptive control is proposed. The advantage of it is that it does not require priori knowledge of virtual control gain function sign, which is usually demanded in many designs. At the same time, the track performance of closed-loop systems is improved by adaptive modifying the estimated error upper bound. By theoretical analysis, the signals of closed-loop systems are globally ultimately bounded in probability and the track error converges to a small residual set around the origin in 4th-power expectation.
基金Project(61673399)supported by the National Natural Science Foundation of ChinaProject(2017JJ2329)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2018zzts550)supported by the Fundamental Research Funds for Central Universities,China
文摘The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.
文摘Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.
基金Project Supported by National Natural Science Foundation of China （50425722）, Natural Science Foundation of CQ CSTC （Chongqing Science and Technology Commission） （2008BA3026）.
文摘The power transformer is the key equipment of transforming voltage and exchanging power in the power system.It's safe and reliable operation directly influences the safe level of the power system.To study the risk assessment of power transformer which is very significant to improve the reliability of the power system,a fuzzy comprehensive risk assessment model of power transformer based on Borda number theory is proposed in this paper.At first,the fault types and risk factors of the power transformer are analyzed.Secondly,the basic framework of the fuzzy comprehensive evaluation is applied to quantify the risk factors.And then,Borda number theory is employed to analyze influence degree and occurrence probability of power transformer.At last,the various risk factors impact index and fuzzy comprehensive evaluation index of power transformer can be easily obtained.Applying this model,the relative importance degree of the risk factors can be horizontally compared according to the numerical index,the engineering staff can directly get the parameters of the transformer risk level and get a good description of the visual expression through using 5 score and similar visual language.
基金Supported by National Natural Science Foundation of China （10571036） the Key Discipline Development Program of Beijing Municipal Commission （XK100080537）
基金Supported by National Basic Research Program of China （973 Program） （2007CB814904）, National Natural Science Foundation of China （10671112, 10701050）, and Natural Science Foundation of Shandong Province （Z2006A01）
基金National Natural Science Foundation of china(60274014,60574088)
文摘Based on the T-S fuzzy model,this paper presents a new model of non-linear network control system with stochastic transfer delay.Sufficient criterion is proposed to guarantee globally asymptotically stability of this two-levels T-S fuzzy model.Also a T-S fuzzy observer of NCS is designed base on this two-levels T-S fuzzy model.All these results present a new approach for networked control system analysis and design.
基金Supported by National Natural Science Foundation of P. R. China (60273028)
文摘This paper combines image processing with 3D magnetic tracking method to develop a scalpel for haptic simulation in surgical cutting. First, a cutting parameter acquisition setup is presented and the performance is validated from soft tissue cutting. Then, based on the acquired input-output data pairs, a method for fuzzy system modeling is presented, that is, after partitioning each input space equally and giving the premises and the total number of fuzzy rules, the consequent parameters and the fuzzy membership functions (MF) of the input variables are learned and optimized via a neurofuzzy modeling technique. Finally, a haptic scalpel implemented with the established cutting model is described. Preliminary results show the feasibility of the haptic display system for real-time interaction.
