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Characterization of Analog Local Cluster Neural Network Hardware for Control

Sitte, Joaquin, Zhang, Liang, & Rueckert, Ulrich (2007) Characterization of Analog Local Cluster Neural Network Hardware for Control. IEEE Transactions on Neural Networks, 18(4), pp. 1242-1253.

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Abstract

The local cluster neural network (LCNN) was designed for analog realization especially suited to applications in control systems. It uses clusters of sigmoidal neurons to generate basis functions that are localized in multidimensional input space. Sigmoidal neurons are well suited to analog electronic realization. In this paper, we report the results of extensive measurements that characterize the computational capabilities of the first analog very large scale integration (VLSI) realization of the LCNN. Despite manufacturing fluctuations and the inherent low precision of analog electronics, the test results suggest that it may be suitable for use in feedback control systems.

Impact and interest:

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112 since deposited on 23 Jul 2008
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ID Code: 14133
Item Type: Journal Article
Keywords: control systems, neural nets
DOI: 10.1109/TNN.2007.899518
ISSN: 1045-9227
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 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: 23 Jul 2008
Last Modified: 29 Feb 2012 23:34

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