A CMOS CURRENT-MODE DESIGN OF MODIFIED LEARNING-VECTOR-QUANTIZATION NEURAL NETWORKS

Abstract

A modification of LVQ model, Modified LVQ (MLVQ) model, is proposed for the estimation of centroid in pattern recognition. Computer simulation results are presented which demonstrate the behavior of the MLVQ model in estimating the class centroid by utilizing the distance-dependent step size. The results indicate the high potential of less dependence on the initial point as well as the precise settlement of the weight vectors to the centroids. The main feature is that the proposed model is robust to the noise perturbation between two pattern distributions in practical applications. To take advantage of this MLVQ model with the faster training and recalling process for patterns, a hybrid analog-digital processing system is designed by the CMOS current-mode integrated circuit (IC) technology and offers the best attributes of both analog and digital computation. This hybrid processing system operates at microsecond time scale, which enables it to produce real time solutions for complex spatiotemporal problems found in high speed signal processing applications. The overall neural processing system has also been simulated and verified by the HSPICE circuit simulator.