Comparative study of fuzzy logic and neural network methods in modeling of simulated steady-state data

Abstract

In this paper fuzzy logic and neural network methods were used to model simulated nonlinear steady-state data. Two different cases of training and checking data sets were generated: ideal data without noise and realistic data with added noise and other nonidealities. Both techniques were fitting the ideal case data almost perfectly. The fuzzy logic and neural network models were also able to roughly predict the realistic case data.