Please use this identifier to cite or link to this item:
|Title:||Tuning of the structure and parameters of a neural network using an improved genetic algorithm|
|Authors:||Leung, Frank H. F.|
Lam, H. K.
Ling, S. H.
Tam, Peter K. S.
|Subjects:||Genetic algorithm (GA)|
|Source:||IEEE transactions on neural networks, Jan. 2003, v. 14, no. 1, p. 79-88.|
|Abstract:||This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). It will also be shown that the improved GA performs better than the standard GA based on some benchmark test functions. A neural network with switches introduced to its links is proposed. By doing this, the proposed neural network can learn both the input-output relationships of an application and the network structure using the improved GA. The number of hidden nodes is chosen manually by increasing it from a small number until the learning performance in terms of fitness value is good enough. Application examples on sunspot forecasting and associative memory are given to show the merits of the improved GA and the proposed neural network.|
|Rights:||© 2003 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 Journal/Magazine Articles|
Files in This Item:
|Structure and parameters of a neural network_03.pdf||432.58 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.