SYSTEMATIC MODELING APPROACH OF FUZZY NEURAL NETWORK

• Main Authors: Lai Li-Hsin, 賴立新
• Other Authors: Ta-Hsiung Hung
• Format: Others
• Language: zh-TW
• Published: 1998
• Online Access: http://ndltd.ncl.edu.tw/handle/90717783755662283727

碩士 === 大同工學院 === 電機工程研究所 === 86 === In this thesis, we propose a systematic method to construct a fuzzy neural network and then apply the proposed network to track a signal or control an unknown plant. The presented method integrates the learning ability of neural network and the advantage of fuzzy logic controller to handle the nonlinear system modeling problems. To model a system, we first apply the grey relational mountain method to find the clustering centers of training data. The grey relational mountain method, based on the grey relational analysis and the mountain method, provides a simple scheme to find the proper clustering centers that have higher relation to the training data. Then we adopt these clustering centers as the initial centers of membership functions in fuzzy neural network. After constructing the architecture of fuzzy neural network, the proposed method can adjust the scaling factors of input/output variables and tune the membership functions by the back propagation algorithm. Finally, the proposed fuzzy neural network is applied to track a signal and control an unknown or complex ill-defined system. Of the learning ability and self-tuning ability, the performance of tracking and on-line control will be confirmed by simulationresults.