Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25259
Title: Variable-structure neural network with real-coded genetic algorithm and its application on short-term load forecasting
Authors: Ling, SH
Leung, FHF 
Iu, HHC
Iu, H
Keywords: Neural network
Short-term load forecasting and real-coded genetic algorithm
Issue Date: 2009
Source: International journal of information and systems sciences, 2009, v. 5, no. 1, p. 23-40 How to cite?
Journal: International journal of information and systems sciences 
Abstract: This paper presents a novel neural network with a variable structure, which is trained by a real-coded genetic algorithm (RCGA), and its application on short-term load forecasting. 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 are introduced in the links between the hidden and output layers. By using the NSC to control the on-off states of the switches in the NNLS, the proposed neural network can model different input patterns with variable network structures. It gives better results and learning ability than the fixed-structure network with link switches (FSNLS) [3], wavelet neural network (WNN) [25] and feed-forward fully-connected neural network (FFCNN) [9]. In this paper, an improved RCGA [2] is used to train the parameters of the VSNN. An industrial application on short-term load forecasting in Hong Kong is given to illustrate the merits of the proposed network.
URI: http://hdl.handle.net/10397/25259
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