On-Chip Genetic Algorithm Optimized Pulse Based RBF Neural Network for Unsupervised Clustering Problem

摘要

This paper presents a new technique for solving the unsupervised clustering problem using a pulse based RBF neural network that is optimized by a genetic algorithm. In this new approach, the neuron encodes the information using the firing times of pulses that are generated by the neurons. Due to hardware's speed advantage and its ability to parallelize along with reprogramability and reconfigurability, the field programmable gate array (FPGA) is used for implementing the proposed approach on-chip. The developed on-chip system is capable of solving various problems requiring large data set in real time. Experimental verification using a sample data set and Fisher's Iris data set has shown the feasibility of the developed system.