A Fuzzy MLP Approach for Nonlinear System Identification

In case of decision making problems, identification nonlinear systems is an important issue. Identification using a multilayer perceptron (MLP) trained with back propagation becomes much complex increase in number input data, layers, nodes, and iterations computation. this paper, attempt has been made to use fuzzy MLP its learning algorithm for system. The training which allows accelerate process training, exceeds comparison classical proposed. Results show sharp reduction search optimal parameters neuro model as compared the MLP. A performance carried out between proposed fuzzy-MLP model. time space complexities algorithms have analyzed. It observed that epochs sharply reduced increased