Comparison of Neural Networks and Fuzzy Logic Control Designed by Multi-objective Genetic Algorithm
نویسندگان
چکیده
In this work we consider the design of neural network and Takagi Sugeno fuzzy logic controller, TSFLC, with Multi-Obejctive Genetic Algorithm, MOGA. For the neural network, the MOGA has to minimize three objectives, the cumulated error, the number of neurons in the hidden layer and the number and type of inputs to the network. In the case of the TS FLC, the objectives are the cumulated error and the parameters of the rules consequence. Both algorithms are applied for the control of temperature system.
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