NDLI: Gauss-Chebyshev neural networks

Content Provider	IEEE Xplore Digital Library
Author	Hong-Jie Xing Bao-Gang Hu
Copyright Year	2005
Description	Author affiliation: Inst. of Autom., Chinese Acad. of Sci., Beijing, China (Hong-Jie Xing; Bao-Gang Hu)
Abstract	This paper presents a novel neural network integrating both Gauss neural network and Chebyshev neural network. The Gauss-Chebyshev neural networks take advantages of the for local approximation ability, but the Chebyshev one for global generalization ability. Numerical experiments confirm the new strategy on the better performance in comparison with Gauss neural networks. Furthermore, under the same initialization conditions, Gauss-Chebyshev neural network is more efficient than Gauss-Sigmoid neural network for regression application. All eight functions tested from the experiments show the improvements of the proposed neural networks.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4110
Ending Page	4115
File Size	348156
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527657
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Gaussian processes Neural networks Chebyshev approximation Feedforward neural networks Gaussian approximation Pattern recognition Kernel Convergence Parametric statistics Laboratories momentum constant Generalization ability Gauss-Chebyshev approximation ability back propagation learning rate
Content Type	Text
Resource Type	Article

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