Please use this identifier to cite or link to this item:
|Title:||Genetic algorithm based variable-structure neural network and its industrial application|
|Authors:||Ling, S. H.|
Leung, Frank H. F.
Lam, H. K.
Electric control equipment
Electric load forecasting
|Source:||IECON 2004 : 30th annual conference of IEEE Industrial Electronics Society : Busan, South Korea, 2-6 November 2004, p. 1273-1278.|
|Abstract:||This paper presents a neural network model with a variable structure, which is trained by an improved genetic algorithm (GA). The proposed variable-structure neural network (VSNN) consists of a Neural Network with Link Switches (NNLS) and a Network Switch Controller (NSC). In the NNLS, switches in its links between the hidden and output layers are introduced. By introducing the NSC to control the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. The proposed network gives better results and increased learning ability than conventional feed-forward neural networks. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.|
|Rights:||© 2004 IEEE. 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.|
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
|Appears in Collections:||EIE Conference Papers & Presentations|
Files in This Item:
|Variable-structure neural network_04.pdf||837.2 kB||Adobe PDF||View/Open|
All items in the PolyU Institutional Repository are protected by copyright, with all rights reserved, unless otherwise indicated. No item in the PolyU IR may be reproduced for commercial or resale purposes.