A neuro-fuzzy system for chemical agent detection

The authors previously introduced a fuzzy version of Kohonen´s well-known self-organizing map neural network model. In this novel neuro-fuzzy system, the neurons of Kohonen´s original model are replaced by fuzzy rules. Each fuzzy rule is composed of fuzzy sets and an output singleton. Since the fuzzy self-organizing map is a modified version of Kohonen´s original model, the self-organizing map and the learning vector quantization learning laws can be used to tune the neuro-fuzzy system. Originally, the fuzzy self-organizing map was intended to be used as an unknown function approximator, while Kohonen´s self-organizing map is primarily used as a neural classifier. In this paper, the authors show how the fuzzy self-organizing map can also be used as a neuro-fuzzy classifier. Simulation results show that, in chemical agent detection, the fuzzy self-organizing map not only gives better classification results than Kohonen´s model, but it also has smaller number of fuzzy rules than the corresponding neurons required by Kohonen´s self-organizing map