Shape Control Learning Algorithm for Neural Networks

Article Preview

Abstract:

A new kind of shape control learning algorithm (SCLA) for training neural networks is proposed. We use the rational cubic spline (with quadratic denominator) to implement a new neural system for shape control, and construct a new kind of artificial neural networks based on given patterns. The shape can be controlled by some shape parameters, which is much different from the known algorithms for training neural networks. The numerical experiments indicate that the new method proposed in this paper demonstrates good results.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

2270-2274

Citation:

Online since:

August 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] D. Y. Zhang, New Theories and Methods on Neural Networks, Beijing: Tsinghua University Press, 2006 (in Chinese).

Google Scholar

[2] D. Y. Zhang, New algorithm for training feedforward neural networks with cubic spline weight functions, Systems Engineering and Electronics, Vol. 28, Sep. 2006, pp.1434-1437 (in Chinese).

Google Scholar

[3] D. Y. Zhang, New algorithm for training neural networks based on generalized Чебышев polynomials, Systems Engineering and Electronics, Vol. 30, Nov. 2008, pp.2274-2279 (in Chinese).

Google Scholar

[4] D. Y. Zhang, A new algorithm of neural networks with B-Spline weight functions, Proc. 2010 International Conference on Artifical Intelligence and Education. Hangzhou: IEEE Press, 2010, pp.782-785.

Google Scholar

[5] R. J. Ning, G. Q. Zhu, The construction of a kind of rational spline, Numerical Mathematics: A Journal of Chinese Universities, Vol. 2, 1996, pp.134-138.

Google Scholar

[6] F. N. Fritsch, R. E. Carlson. Monotone piecewise cubic interpolation, SIAM Journal on Numerical Analysis, Vol. 17, Feb. 1980, pp.238-246.

DOI: 10.1137/0717021

Google Scholar