CMOS design of robust neural chip with the on-chip learning capability

Chung-Yu Wu*, Ron Yi Liu, I. Chang Jou, Famn Jiang Shyh Jye

*Corresponding author for this work

Research output: Contribution to journalConference article

Abstract

A robust neural model - Modified Learning Vector Quantization (MLVQ) - is proposed for the estimation of centroid in pattern recognition. This MLVQ model can significantly demonstrate the behavior of better estimating the class centroid by utilizing the distance-dependent step size. The computer simulation result can 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 of this model is robust to the noise perturbation to the pattern distributions in practical applications. Also, a mixed-mode of analog-digital processing systems are designed by the CMOS current-mode VLSI technology and offer the best attributes of both analog and digital computation. The final experimental results of this hybrid processing systems can show the on-chip learning capability and operate at microsecond time scale to achieve the goal of real-time neural applications.

Original languageEnglish
Article number5450078
Pages (from-to)426-429
Number of pages4
JournalProceedings - IEEE International Symposium on Circuits and Systems
Volume3
DOIs
StatePublished - 1 Jan 1996
EventProceedings of the 1996 IEEE International Symposium on Circuits and Systems, ISCAS. Part 1 (of 4) - Atlanta, GA, USA
Duration: 12 May 199615 May 1996

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