Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12518
Title: Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications
Authors: Ling, SH
Leung, FHF 
Lam, HK
Keywords: Hand-written recognition
Neural network
Real-coded genetic algorithm
Short-term load forecasting
Issue Date: 2007
Publisher: Springer
Source: Soft computing, 2007, v. 11, no. 11, p. 1033-1052 How to cite?
Journal: Soft Computing 
Abstract: This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.
URI: http://hdl.handle.net/10397/12518
DOI: 10.1007/s00500-007-0151-5
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