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A New Fuzzy Neural Network and Its Applications to Fuzzy Control

Author: XuYingZhi
Tutor: ZhangJunYing
School: Xi'an University of Electronic Science and Technology
Course: Applied Computer Technology
Keywords: Fuzzy Systems Neural Networks Fuzzy Neural Network Neuro-fuzzy control Fuzzy Multilayer Perceptron
CLC: TP183
Type: Master's thesis
Year: 2008
Downloads: 146
Quote: 3
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Articulate empirical knowledge of the fuzzy system can deal with the fuzziness of information , which is a neural network can not do ; but on the other hand fuzzy system design parameters of the rules and membership functions to select only rely on the experience , it is difficult to automatic design and adjustment, this is the main drawback of the fuzzy system . So, if we use neural networks to construct the fuzzy system , you can make use of neural network learning methods , based on the input and output sample design and adjust fuzzy system design parameters , to achieve self-learning function of the fuzzy system . This paper proposes a average (Average) fuzzy operator to calculate the degree of each rule applies fuzzy reasoning model , and the fuzzy inference system to construct a hierarchical neural network . The network uses the steepest descent learning method , using a sample of numerical information adaptively adjust fuzzy membership function parameters and rules excitation intensity , automatic extraction of fuzzy if-then rules , in order to achieve self- learning and adaptive function of the fuzzy system . The system is similar in structure to the MLP neural network is a fuzzy system , while in function , which we call the FMLP - ( Fuzzy Multilayer Perceptrons ) . Simulation experiments confirmed FMLP reasonable structure and effectiveness of the algorithm . Fuzzy controller 's performance is mainly limited to whether it can find a suitable fuzzy rules . In this paper, FMLP to adaptive neuro-fuzzy control system design does not rely on expert experience . Successfully implements adaptive fuzzy control with adaptive inverse control using the adaptive neural fuzzy control method in the simulation examples .

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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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