Local Cluster Neural Network On-Chip Training

Zang, Liang & Sitte, Joaquin (2006) Local Cluster Neural Network On-Chip Training. In Bonissone, P., Wang, L., & Lucas, S. (Eds.) 2006 IEEE World Congress on Computational Intelligence, 16-21 July, British Columbia, Canada.

Abstract

The local cluster neural network is a feedforward RBF network that has been implemented in analogue neural net chip. The LCNN chip can be trained by chip-in-the-loop training and this training method has been demonstrated to work efficiently. In order to increase the functionality of LCNN chip, we proposed on-chip training for the LCNN chip. In this paper, we describe two training algorithms – Gradient Descent and Probabilistic Random Weight Change, which are used in LCNN on-chip training simulations. We also present the experiment results from the simulations in multi-dimensional function approximation. The training convergence is investigated and analyzed. The circuite signal flow chart for these two algorithms are designed.

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ID Code: 10171
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
ISBN: 0780394985
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 16 Oct 2007 00:00
Last Modified: 29 Feb 2012 13:25

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