Modelling of nonlinear process dynamics using Kohonen's Neural Networks, Fuzzy Systems and Chebyshev Series

摘要

This paper introduces a new approach to the problem of nonlinear system identification with the aid of neural networks, fuzzy systems and truncated Chebyshev series. The proposed methodology is of general use and results in both a linguistic and an analytical model of the system under study. The method was successfully used for identifying certain operating regions of a Continuous Stirred Tank Reactor (CSTR) where highly nonlinear phenomena, such as limit cycles and multiple steady states appear.