Design on supervised / unsupervised learning reconfigurafole digital neural network structure

Abstract

We propose a reconfigurable neural network structure which has capability to process supervised or unsupervised learning algorithm computation. The proposed structure is based on modular structure which can configure artificial neural network architecture flexibly. Main processing unit of the proposed structure is designed to obtain flexibility of its internal structure by specific instructions. Therefore it is possible to configure MLP (Multi-Layer Perceptron) with back-propagation for alphabet recognition and parallel SOM for impulse noise detection problem. The performance comparison with the matlab simulation shows its value in the aspects of reliability. © Springer-Verlag Berlin Heidelberg 2006.