IG-Based Genetically Optimized Fuzzy Polynomial Neural Networks

Abstract

In this paper, we introduce a neo scheme of fuzzy-neural networks – Fuzzy Polynomial Neural Networks (FPNN) with a new fuzzy set-based polynomial neurons (FSPNs) whose fuzzy rules include the information granules (about the real system) obtained through Information Granulation(IG). We investigate the proposed networks from two different aspects to improve the performance of the fuzzy-neural networks. First, We have developed a design methodology (genetic optimization using Genetic Algorithms) to find the optimal structure for fuzzy-neural networks that expanded from Group Method of Data Handling (GMDH). It is the number of input variables, the order of the polynomial, the number of membership functions, and a collection of the specific subset of input variables that are the parameters of FPNN fixed by aid of genetic optimization that has search capability to find the optimal solution on the solution space. Second, we have been interested in the architecture of fuzzy rules that mimic the real world, namely sub-model (node) composing the fuzzy-neural networks. We adopt fuzzy set-based fuzzy rules as substitute for fuzzy relation-based fuzzy rules and apply the concept of Information Granulation to the proposed fuzzy set-based rules. The performance of genetically optimized FPNN (gFPNN) with fuzzy set-based polynomia neurons (FSPNs) composed of fuzzy set-based rules is quantified through experimentation where we use a number of modeling benchmarks data which are already experimented with in fuzzy or neurofuzzy modeling.KeywordsFuzzy RuleInformation GranulationConsequent PartFuzzy IdentificationInput Variable SelectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.