Title :
Gauss-Chebyshev neural networks
Author :
Xing, Hong-Jie ; Hu, Bao-Gang
Author_Institution :
Inst. of Autom., Chinese Acad. of Sci., Beijing, China
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.
Keywords :
Chebyshev approximation; backpropagation; generalisation (artificial intelligence); neural nets; Gauss kernel; Gauss-Chebyshev neural networks; backpropagation; global generalization; initialization condition; learning; local approximation; momentum constant; Chebyshev approximation; Convergence; Feedforward neural networks; Gaussian approximation; Gaussian processes; Kernel; Laboratories; Neural networks; Parametric statistics; Pattern recognition; Gauss-Chebyshev; Generalization ability; approximation ability; back propagation; learning rate; momentum constant;
Conference_Titel :
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Conference_Location :
Guangzhou, China
Print_ISBN :
0-7803-9091-1
DOI :
10.1109/ICMLC.2005.1527657