Title :
Design of fuzzy systems using neurofuzzy networks
Author :
Figueiredo, Maurício ; Gomide, Fernando
Author_Institution :
Unicamp-Feec-Dca, Campinas, Brazil
fDate :
7/1/1999 12:00:00 AM
Abstract :
Introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, non-noisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view
Keywords :
computational complexity; function approximation; fuzzy logic; fuzzy systems; inference mechanisms; knowledge based systems; neural nets; accuracy; complexity; fuzzy reasoning principles; fuzzy system design; general neuron model; if-then fuzzy rules; membership functions; neurofuzzy networks; universal approximation capability; Brazil Council; Design methodology; Design optimization; Function approximation; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Neurons; Shape;
Journal_Title :
Neural Networks, IEEE Transactions on