Evolvable Subsethood Product Fuzzy Neural Network for Pattern Classification

This paper presents an evolvable version of a novel subsethood product fuzzy neural inference system (ESuPFuNIS). The original SuPFuNIS model20 employs only fuzzy weights, and accepts both numeric and linguistic inputs. All numeric inputs are fuzzified using a feature specific fuzzifier. The model composes fuzzy signals from the input layer with fuzzy weights using a mutual subsethood measure. Rule nodes use a product aggregation operator. Outputs from the network are generated using volume defuzzification. Here we replace the original gradient descent learning procedure with a genetic optimization technique and report considerable improvements in classification accuracy and rule economy on three benchmark problems. Real-coded genetic algorithms (RGA's) have been employed to search for an optimal set of network parameters. We demonstrate the classification capabilities of the network on Ripley's synthetic two class data, Iris data and Forensic glass data. In all the problems considered, the GA based classifier performs better than its gradient descent counterpart in terms of classification accuracy as well as rule economy.