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Fuzzy optimization neural network model based on LM algorithm 认领 引用 被引量：1
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作者 彭勇 周惠成 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第3期431-436,共6页
A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In ... A new fuzzy optimization neural network model is proposed based on the Levenberg-Marquardt (LM) algorithm on account of the disadvantages of slow convergence of traditional fuzzy optimization neural network model. In this new model,the gradient descent algorithm is replaced by the LM algorithm to obtain the minimum of output errors during network training,which changes the weights adjusting equations of the network and increases the training speed. Moreover,to avoid the results yielding to local minimum,the transfer function is also revised to sigmoid function. A case study is utilized to validate this new model,and the results reveal that the new model fast training speed and better forecasting capability. 展开更多
关键词 fuzzy optimization neural network Levenberg-Marquardt algorithm transfer function
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Manipulator Neural Network Control Based on Fuzzy Genetic Algorithm 认领 引用 被引量：1
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作者 崔平远 Yang Guojun 《High Technology Letters》 EI CAS 2001年第1期63-66,共4页
The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update th... The three-layer forward neural networks are used to establish the inverse kinematics models of robot manipulators. The fuzzy genetic algorithm based on the linear scaling of the fitness value is presented to update the weights of neural networks. To increase the search speed of the algorithm, the crossover probability and the mutation probability are adjusted through fuzzy control and the fitness is modified by the linear scaling method in FGA. Simulations show that the proposed method improves considerably the precision of the inverse kinematics solutions for robot manipulators and guarantees a rapid global convergence and overcomes the drawbacks of SGA and the BP algorithm. 展开更多
关键词 Inverse kinematics Neural networks Fuzzy control Genetic algorithm Fitness function
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Adaptive control of parallel manipulators via fuzzy-neural network algorithm 认领 引用 被引量：3
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作者 Dachang ZHU Yuefa FANG 《控制理论与应用（英文版）》 2007年第3期295-300,共6页
This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric u... This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF. 展开更多
关键词 Parallel manipulator Adaptive control Fuzzy neural network algorithm Simulation
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Development of an electrode intelligent design system based on adaptive fuzzy neural network and genetic algorithm 认领 引用
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作者 Huang Jun Xu Yuelan +1 位作者 Wang Luyuan Wang Kehong 《China Welding》 EI CAS 2014年第2期62-66,共5页
The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical propertie... The coating on the electrodes contains many kinds of raw materials which affect significantly on the mechanical properties of deposited metals. It is still a problem how to predict and control the mechanical properties of deposited metals directly according to the components of coating on the electrodes. In this paper an electrode intelligent design system is developed by means of fuzzy neural network technology and genetic algorithm,, dynamic link library, object linking and embedding and multithreading. The front-end application and customer interface of the system is realized by using visual C ＋＋ program language and taking SQL Server 2000 as background database. It realizes series functions including automatic design of electrode formula, intelligent prediction of electrode properties, inquiry of electrode information, output of process report based on normalized template and electronic storage and search of relative files. 展开更多
关键词 electrode design system adaptive fuzzy neural network genetic algorithm object linking and embedding
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Simultaneous Perturbation Stochastic Approximation Algorithm Combined with Neural Network and Fuzzy Simulation 认领 引用
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作者 宁玉富 唐万生 郭长友 《Transactions of Tianjin University》 EI CAS 2008年第1期43-49,共7页
In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochasti... In order to solve three kinds of fuzzy programm model, fuzzy chance-constrained programming mode ng models, i.e. fuzzy expected value and fuzzy dependent-chance programming model, a simultaneous perturbation stochastic approximation algorithm is proposed by integrating neural network with fuzzy simulation. At first, fuzzy simulation is used to generate a set of input-output data. Then a neural network is trained according to the set. Finally, the trained neural network is embedded in simultaneous perturbation stochastic approximation algorithm. Simultaneous perturbation stochastic approximation algorithm is used to search the optimal solution. Two numerical examples are presented to illustrate the effectiveness of the proposed algorithm. 展开更多
关键词 fuzzy variable fuzzy programming fuzzy simulation neural network approximation theory perturbation techniques computer simulation simultaneous perturbation stochasticapproximation algorithm
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Applying Neural Network withGenetic Algorithm and FuzzySelection Models to Select Equipmentsfor Fully-Mechanized Coal Mining 认领 引用
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作者 王新宇 吴瑞明 冯春花 《Journal of China University of Mining and Technology》 2004年第2期147-151,共5页
According to the typical engineering samples, a neural net work model with genetic algorithm to optimize weight values is put forward to forecast the productivities and efficiencies of mining faces. By this model we c... According to the typical engineering samples, a neural net work model with genetic algorithm to optimize weight values is put forward to forecast the productivities and efficiencies of mining faces. By this model we can obtain the possible achievements of available equipment combinations under certain geological situations of fully-mechanized coal mining faces. Then theory of fuzzy selection is applied to evaluate the performance of each equipment combination. By detailed empirical analysis, this model integrates the functions of forecasting mining faces' achievements and selecting optimal equipment combination and is helpful to the decision of equipment combination for fully-mechanized coal mining. 展开更多
关键词 genetic algorithm artificial neural network fuzzy selection selection of equipment combination
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The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network 认领 引用
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作者 李波 张世英 李银惠 《Journal of Systems Engineering and Electronics》 2002年第3期46-51,共6页
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
关键词 Complex system modeling General stochastic neural network MTS fuzzy model Expectation-maximization algorithm
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Integration of Learning Algorithm on Fuzzy Min-Max Neural Networks 认领 引用
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作者 胡静 罗宜元 《Journal of Shanghai Jiaotong university(Science)》 EI 2017年第6期733-741,共9页
An integrated fuzzy min-max neural network(IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering,pure c... An integrated fuzzy min-max neural network(IFMMNN) is developed to avoid the classification result influenced by the input sequence of training samples, and the learning algorithm can be used as pure clustering,pure classification, or a hybrid clustering classification. Three experiments are designed to realize the aim. The serial input of samples is changed to parallel input, and the fuzzy membership function is substituted by similarity matrix. The experimental results show its superiority in contrast with the original method proposed by Simpson. 展开更多
关键词 fuzzy min-max neural network(FMMNN) supervised and unsupervised learning clustering and classification learning algorithm similarity
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Fuzzy Optimization of an Elevator Mechanism Applying the Genetic Algorithm and Neural Networks 认领 引用 被引量：2
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作者 XI Ping-yuan WANG Bing +1 位作者 SHENTU Liu-fang HU Heng-yin 《International Journal of Plant Engineering and Management》 EI 2005年第4期236-240,共5页
Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth ... Considering the indefinite character of the value of design parameters and being satisfied with load-bearing capacity and stiffness, the fuzzy optimization mathematical model is set up to minimize the volume of tooth corona of a worm gear in an elevator mechanism. The method of second-class comprehensive evaluation was used based on the optimal level cut set, thus the optimal level value of every fuzzy constraint can be attained; the fuzzy optimization is transformed into the usual optimization. The Fast Back Propagation of the neural networks algorithm are adopted to train feed-forward networks so as to fit a relative coefficient. Then the fitness function with penalty terms is built by a penalty strategy, a neural networks program is recalled, and solver functions of the Genetic Algorithm Toolbox of Matlab software are adopted to solve the optimization model. 展开更多
关键词 elevator mechanism fuzzy design optimization genetic algorithm and neural networks toolbox
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Application of hybrid coded genetic algorithm in fuzzy neural network controller 认领 引用
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作者 杨振强 杨智民 +2 位作者 王常虹 庄显义 宁慧 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2000年第1期65-68,共4页
Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during... Presents the fuzzy neural network optimized by hybrid coded genetic algorithm of decimal encoding and binary encoding, the searching ability and stability of genetic algorithms enhanced by using binary encoding during the crossover operation and decimal encoding during the mutation operation, and the way of accepting new individuals by probability adopted, by which a new individual is accepted and its parent is discarded when its fitness is higher than that of its parent, and a new individual is accepted by probability when its fitness is lower than that of its parent. And concludes with calculations made with an example that these improvements enhance the speed of genetic algorithms to optimize the fuzzy neural network controller. 展开更多
关键词 genetic algorithm fuzzy neural network cost function hybrid coding
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Flatness predictive model based on T-S cloud reasoning network implemented by DSP 认领 引用 被引量：5
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作者 ZHANG Xiu-ling GAO Wu-yang +1 位作者 LAI Yong-jin CHENG Yan-tao 《Journal of Central South University》 SCIE EI CAS CSCD 2017年第10期2222-2230,共9页
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita... The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter. 展开更多
关键词 T-S cloud reasoning neural network cloud model flatness predictive model hardware implementation digital signal processor genetic algorithm and simulated annealing algorithm (GA-SA)
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Agricultural Water Resources Utilization and Management under Agricultural Safety Aim Based on Fuzzy Neural Network Algorithm 认领 引用
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作者 Shengxin WAN 《Asian Agricultural Research》 2021年第12期1-4,8,共4页
[Objectives]To explore the agricultural water resources utilization and management under the agricultural safety aim.[Methods]Fuzzy neural network algorithm was adopted.The evaluation model of agricultural water resou... [Objectives]To explore the agricultural water resources utilization and management under the agricultural safety aim.[Methods]Fuzzy neural network algorithm was adopted.The evaluation model of agricultural water resources utilization and management carrying capacity based on quantitative system was established.[Results]With the remarkable improvement of China's national income,great progress has been made in China's agricultural development.However,in the process of agricultural safety production,the problem of sustainable development has not been noticed,the problem of water resources exceeding the limit bearing capacity frequently occurs.[Conclusions]It is of great significance to effectively solve the problem of water resources utilization and management.In the feasibility test for the algorithm,further tests on various indicators show that the research is feasible. 展开更多
关键词 Fuzzy neural network algorithm Quantification system Utilization and management of water resources
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FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK 认领 引用 被引量：2
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作者 李如强 陈进 伍星 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2006年第1期99-108,共10页
A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from ... A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks. 展开更多
关键词 rotating machinery fault diagnosis rough sets theory fuzzy sets theory generic algorithm knowledge-based fuzzy neural network
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Modeling of Energy Consumption and Effluent Quality Using Density Peaks-based Adaptive Fuzzy Neural Network 认领 引用 被引量：10
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作者 Junfei Qiao Hongbiao Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2018年第5期968-976,共9页
Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a... Modeling of energy consumption(EC) and effluent quality(EQ) are very essential problems that need to be solved for the multiobjective optimal control in the wastewater treatment process(WWTP). To address this issue, a density peaks-based adaptive fuzzy neural network(DP-AFNN) is proposed in this study. To obtain suitable fuzzy rules, a DP-based clustering method is applied to fit the cluster centers to process nonlinearity.The parameters of the extracted fuzzy rules are fine-tuned based on the improved Levenberg-Marquardt algorithm during the training process. Furthermore, the analysis of convergence is performed to guarantee the successful application of the DPAFNN. Finally, the proposed DP-AFNN is utilized to develop the models of EC and EQ in the WWTP. The experimental results show that the proposed DP-AFNN can achieve fast convergence speed and high prediction accuracy in comparison with some existing methods. 展开更多
关键词 Density peaks clustering effluent quality （EQ） energy consumption （EC） fuzzy neural network improved Levenberg-Marquardt algorithm wastewater treatment process （WWTP）.
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Fuzzy ARTMAP neural network for seafloor classification from multibeam sonar data 认领 引用 被引量：2
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作者 周兴华 Chen Yongqi +1 位作者 Nick Emerson Du Dewen 《High Technology Letters》 EI CAS 2006年第2期219-224,共6页
This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed ... This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data. 展开更多
关键词 Fuzzy ARTMAP neural network genetic algorithms seafloor classification multibeam sonar
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Using fuzzy neural networks for RMB/USD real exchange rate forecasting 认领 引用 被引量：2
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作者 惠晓峰 李喆 魏庆泉 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第2期189-192,共4页
In order to aim at improving the forecasting performance of the RMB/USD exchange rate, this paper proposes a new architecture of fuzzy neural networks based on fuzzy logic, and the method of point differential, which ... In order to aim at improving the forecasting performance of the RMB/USD exchange rate, this paper proposes a new architecture of fuzzy neural networks based on fuzzy logic, and the method of point differential, which guarantees not only the direction of weight correction, but also the needed precision for the BP algorithm. In applying genetic algorithms for optimal performance, this approach, in the forecasting of the RMB/USD real exchange rate from 1994 to 2000, obviously outperforms typical BP Neural Networks and exhibits a higher capacity in regard to nonlinear, time-variablility, and illegibility of the exchange rate. 展开更多
关键词 fuzzy neural networks fuzzy logic genetic algorithm RMB/USD real exchange rate
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Strategies for Optimizing Feed Rate of Fed-Batch Yeast Fermentation by Fuzzy-Neural Network 认领 引用 被引量：2
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作者 苗志奇 元英进 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 1998年第4期62-69,共8页
In this paper,a novel fuzzy neural network model,in which an adjustable fuzzy sub-space was designed by uniform design,has been established and used in fed-batch yeast fermentationas an example.A brand-new optimizatio... In this paper,a novel fuzzy neural network model,in which an adjustable fuzzy sub-space was designed by uniform design,has been established and used in fed-batch yeast fermentationas an example.A brand-new optimization sub-network with special structure has been built andgenetic algorithm,guaranteeing the optimization in overall space,is introduced for the feed rateoptimization.On the basis of the model network,the optimal substrate concentration and theoptimal amount of fed-batch at different periods have been studied,aided with the optimizationnetwork and the genetic algorithm separately.The above results can be used as a basis for theestablishment of a fuzzy neural network controller. 展开更多
关键词 fuzzy neural network optimization fed-batch fermentation the genetic algorithm
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Fuzzy Control System of Hydraulic Roll Bending Based on Genetic Neural Network 认领 引用 被引量：2
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作者 JIAChun-yu LIUHong-min ZHOUHui-feng 《Journal of Iron and Steel Research International》 SCIE CAS 2005年第3期22-27,共6页
For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system ... For nonlinear hydraulic roll bending control, a new fuzzy intelligent control method was proposed based on the genetic neural network. The method taking account of dynamic and static characteristics of control system has settled the problems of recognizing and controlling the unknown, uncertain and nonlinear system successfully, and has been applied to hydraulic roll bending control. The simulation results indicate that the system has good performance and strong robustness, and is better than traditional PID and neural-fuzzy control. The method is an effective tool to control roll bending force with increased dynamic response speed of control system and enhanced tracking accuracy. 展开更多
关键词 genetic algorithm neural network fuzzy control hydraulic roll bending shape
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FUZZY NEURAL NETWORK FOR MACHINE PARTS RECOGNITION SYSTEM 认领 引用 被引量：2
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作者 Luo XiaobinYin GuofuChen KeHu Xiaobing Luo YangInstitute of CAD&CAM,Sichuan University,Chengdu 610065, China 《Chinese Journal of Mechanical Engineering》 EI CAS 2003年第3期334-336,共3页
The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it ... The primary purpose is to develop a robust adaptive machine parts recognitionsystem. A fuzzy neural network classifier is proposed for machine parts classifier. It is anefficient modeling method. Through learning, it can approach a random nonlinear function. A fuzzyneural network classifier is presented based on fuzzy mapping model. It is used for machine partsclassification. The experimental system of machine parts classification is introduced. A robustleast square back-propagation (RLSBP) training algorithm which combines robust least square (RLS)with back-propagation (BP) algorithm is put forward. Simulation and experimental results show thatthe learning property of RLSBP is superior to BP. 展开更多
关键词 Fuzzy neural network Image processing RLSBP algorithm Machine partsclassification
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HPSO-based fuzzy neural network control for AUV 认领 引用 被引量：2
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作者 Lei ZHANG Yongjie PANG Yumin SU Yannan LIANG 《控制理论与应用（英文版）》 2008年第3期322-326,共5页
A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimiz... A fuzzy neural network controller for underwater vehicles has many parameters difficult to tune manually. To reduce the numerous work and subjective uncertainties in manual adjustments, a hybrid particle swarm optimization （HPSO） algorithm based on immune theory and nonlinear decreasing inertia weight （NDIW） strategy is proposed. Owing to the restraint factor and NDIW strategy, an HPSO algorithm can effectively prevent premature convergence and keep balance between global and local searching abilities. Meanwhile, the algorithm maintains the ability of handling multimodal and multidimensional problems. The HPSO algorithm has the fastest convergence velocity and finds the best solutions compared to GA, IGA, and basic PSO algorithm in simulation experiments. Experimental results on the AUV simulation platform show that HPSO-based controllers perform well and have strong abilities against current disturbance. It can thus be concluded that the proposed algorithm is feasible for application to AUVs. 展开更多
关键词 Autonomous underwater vehicle Fuzzy neural network Model reference adaptive control Particle swarm optimization algorithm Immune theory
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