Please use this identifier to cite or link to this item:
Title: An improved genetic-algorithm-based neural-tuned neural network
Authors: Leung, FHF 
Ling, SH
Lam, HK
Keywords: Genetic algorithm
Neural network
Pattern recognition
Sunspot forecasting
Issue Date: 2008
Publisher: Imperial College Press
Source: International journal of computational intelligence and applications, 2008, v. 7, no. 4, p. 469-492 How to cite?
Journal: International journal of computational intelligence and applications 
Abstract: This paper presents a neural-tuned neural network (NTNN), which is trained by an improved genetic algorithm (GA). The NTNN consists of a common neural network and a modified neural network (MNN). In the MNN, a neuron model with two activation functions is introduced. An improved GA is proposed to train the parameters of the proposed network. A set of improved genetic operations are presented, which show superior performance over the traditional GA. The proposed network structure can increase the search space of the network and offer better performance than the traditional feed-forward neural network. Two application examples are given to illustrate the merits of the proposed network and the improved GA.
ISSN: 1469-0268
EISSN: 1757-5885
DOI: 10.1142/S1469026808002375
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record


Last Week
Last month
Citations as of Aug 15, 2018

Page view(s)

Last Week
Last month
Citations as of Aug 19, 2018

Google ScholarTM



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