Evolutionary Subsethood Product Fuzzy Neural Network

This paper employs a simple genetic algorithm (GA) to search for an optimal set of parameters for a novel subsethood product fuzzy neural network introduced elsewhere, and to demonstrate the pattern classification capabilities of the network. The search problem has been formulated as an optimization problem with an objective to maximize the number of correctly classified patterns. The performance of the network, with GA evolved parameters, is evaluated by computer simulations on Ripley's synthetic two class data. The network performed excellently by being at par with the Bayes optimal classifier, giving the best possible error rate of 8%. The evolutionary subsethood product network outperformed all other models with just two rules.