Training of neurofuzzy power system stabilisers using genetic algorithms
作者： A. Afzalian , D.A. Linkens
DOI: 10.1016/S0142-0615(99)00042-3
关键词: Fuzzy control system 、 Control engineering 、 Control theory 、 Fuzzy logic 、 Multilayer perceptron 、 Electric power system 、 Control theory 、 Artificial neural network 、 Engineering 、 Genetic algorithm 、 Fitness function
摘要: The problem of selecting and tuning the parameters a neurofuzzy controller using genetic algorithms is discussed in this paper. implemented as multilayer perceptron, which weights are fuzzy membership functions. optimal values if-part then-part functions have been found during learning method by applying an appropriate fitness function based on controlled plant output. proposed has applied to optimise power system stabiliser (NF PSS). overall tested simulation model different operating conditions improved responses achieved.
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