A nonlinear system identification approach based on Fuzzy Wavelet Neural Network

Abstract

This paper concerns the use of an alternative Fuzzy Wavelet Neural Network (FWNN) to model the input-output maps of nonlinear dynamic systems. The analyzed structure uses only wavelet functions in the consequent part of its fuzzy rules. The advantages and disadvantages of using this FWNN in model identification tasks are listed considering a comparative study performed with other FWNN structures found in literature. The evaluations are carried out using a real multisection liquid storage tank with abrupt transitions between its sections. The analysis is based on usual criteria such as: mean quadratic error, number of training epochs, number of adjustable parameters, quadratic error variance, among others. The results indicate that the modified FWNN structure maintains the capability of generalization and other important characteristics presented by traditional networks FWNN, despite the reduction in the complexity of the structure.