Artificial Immune System for Fuzzy Backpropagation Neural Networks Optimization

Abstract

Fuzzy Neural Networks (FNNs) enhance conventional Artificial Neural Networks (ANNs) by incorporating fuzzy membership functions, which enable the handling of uncertainty, ambiguity, and imprecise information. While Fuzzy Backpropagation Neural Networks (FBNNs) improve classification performance across noisy datasets, the effectiveness of fuzzification heavily depends on the proper tuning of membership function parameters—typically optimized manually. This paper presents a novel Artificial Immune System framework for optimizing Fuzzy Backpropagation Neural Networks used in the classification of biological image data. The approach integrates a fuzzy min–max fuzzification layer with a feed‑forward backpropagation network and applies an optimization version of an Artificial Immune Network model, derived from opt‑aiNet, to tune trapezoidal membership functions. Experimental results confirm that the proposed immune‑driven optimization is an effective technique for enhancing FBNN robustness and generalization.