A Nonlinear Fuzzy Neural Network Modeling Approach Using an Improved Genetic Algorithm

Fuzzy neural networks (FNNs) are quite useful for nonlinear system identification when only the input/output information is available. A new FNN framework is first proposed by combining an AutoRegressive with exogenous input (ARX) with the nonlinear Tanh function in the Takagi-Sugeno (T-S) type fuzz...

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Veröffentlicht in:	IEEE transactions on industrial electronics (1982) 2018-07, Vol.65 (7), p.5882-5892
Hauptverfasser:	Zhang, Ridong, Tao, Jili
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Clustering algorithms Coke fired furnaces Computer simulation Decoding Fuzzy logic Fuzzy neural networks Fuzzy systems Genetic algorithm (GA) Genetic algorithms Mathematical model Mathematical models Modelling neural network Neural networks nonlinear system modeling Nonlinear systems Optimization Parameters System identification Takagi–Sugeno (T-S) fuzzy model
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