Please use this identifier to cite or link to this item:
PIRA download icon_1.1View/Download Full Text
Title: Tuning of the structure and parameters of neural network using an improved genetic algorithm
Authors: Lam, HK
Ling, SH
Leung, FHF 
Tam, PKS
Issue Date: 2001
Source: IECON'01 : the 27th annual conference of the IEEE Industrial Electronics Society : Denver, Colorado, USA, Nov 29 (Thu) to Dec 2 (Sun) 2001, p. 25-30
Abstract: This paper presents the tuning of the structure and parameters of a neural network using an improved genetic algorithm (GA). The improved GA is implemented by floating-point number. The processing time of the improved GA is faster than that of the GA implemented by binary number as coding and decoding are not necessary. By introducing new genetic operators to the improved GA, it will also be shown that the improved GA performs better than the traditional GA based on some benchmark test functions. A neural network with switches introduced to 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 structure and the parameters of the neural network can be tuned. An application example on sunspot forecasting is given to show the merits of the improved GA and the proposed neural network.
Keywords: Computer simulation
Encoding (symbols)
Genetic algorithms
Parameter estimation
Publisher: IEEE
ISBN: 0-7803-7108-9
Rights: © 2001 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:Conference Paper

Files in This Item:
File Description SizeFormat 
Parameters of neural network_01.pdf437.15 kBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

Last Week
Last month
Citations as of Jun 4, 2023


Citations as of Jun 4, 2023


Last Week
Last month
Citations as of Jun 8, 2023

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.