Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17184
Title: An improved genetic-algorithm-based neural-tuned neural network
Authors: Leung, FHF 
Ling, SH
Lam, HK
Issue Date: 2008
Source: International journal of computational intelligence and applications, 2008, v. 7, no. 4, p. 469-492
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.
Keywords: Genetic algorithm
Neural network
Pattern recognition
Sunspot forecasting
Publisher: Imperial College Press
Journal: International journal of computational intelligence and applications 
ISSN: 1469-0268
EISSN: 1757-5885
DOI: 10.1142/S1469026808002375
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