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Research on Genetic Optimization_Based Neural Network Control Strategy

Author: ChenHaiJun
Tutor: JiangWanLu
School: Yanshan University
Course: Mechanical and Electronic Engineering
Keywords: Neural Networks Genetic Algorithms Inverse System Identification Composite Inverse Control Electro-hydraulic servo system MATLAB Labview
CLC: TP183
Type: Master's thesis
Year: 2010
Downloads: 206
Quote: 3
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Abstract


With the development of automatic control science , the increasing demands of the people of the quality of the control system , and control with nonlinear uncertainties and difficult to establish a precise mathematical model complex systems , the traditional control strategy has increasingly shown its limitations at the same time the development of intelligent control theory for solving such problems new research ideas . Composite inverse control strategy based on genetic algorithm optimization neural network , the integration of genetic algorithms and artificial neural networks to solve the current control problems in the field of research . Artificial neural network with nonlinear mapping , parallel computing , self - learning ability and strong robustness in complex nonlinear system modeling , parameter optimization and control field widely . BP neural network learning problems of multilayer study . Based on gradient descent BP learning algorithm existence of local convergence problems , the introduction of genetic algorithm to solve the neural network of the right value , the threshold learning proposed improved BP neural network learning algorithm genetic optimization learning algorithm to improve the network learning accuracy . And inverse system of thought control guidance composite inverse learning neural network based on genetic optimization control strategy . In this paper, the typical material testing machine of electro-hydraulic servo system to control genetic optimization of neural network - based composite inverse control policy is applied to the material testing machine position servo system , the application of neural networks to identify the dynamic inverse model by simulation and experimental research through the development of a computer-controlled system based on Labview software in the MATLAB environment , and contrast with the traditional PID control strategy , and the results show that genetic optimization of neural network - based composite inverse control strategy can be used to meet the material testing machine requirements of the dynamic characteristics .

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory > Artificial Neural Networks and Computing
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