NDLI: An improved algorithm of Neural Networks with Cubic Spline Weight Function

Content Provider	IEEE Xplore Digital Library
Author	Liu Keyuan Li Haibin He Yan Duan Zhixin
Copyright Year	2010
Description	Author affiliation: Science College, Inner Mongolia University of Technology, Hohhot 010051, China (Liu Keyuan; Li Haibin; He Yan; Duan Zhixin)
Abstract	The paper proposes an improved Neural Networks construction with Cubic Spline Weight Function and its algorithm for the characteristic of the poor extending ability of the Neural Networks with Cubic Spline Weight Function. The Weight Function is divided into two parts by the improved algorithm. The Weight Function is trained by the three Cubic Spline Weight Function of the original algorithm and the constant coefficient of the Weight Function is trained by the grad dropping method. Because the new algorithm combines the merits of the Cubic Spline Weight Function Neural Networks with the merits of the traditional Neural Networks, the problems of the traditional dropping algorithm, such as the local minimum, slow convergence rate and initial value sensitivity, are not existed and the extending ability is better. The results of the simulation shows that compared to the traditional algorithm, the algorithm has high precision, fast speed and the extending ability remarkably improved compared to the unimproved algorithm.
Starting Page	2673
Ending Page	2677
File Size	227759
Page Count	5
File Format	PDF
ISBN	9781424451814
DOI	10.1109/CCDC.2010.5498738
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neurons Transfer functions Neural Networks with Cubic Spline Weight Function Educational institutions Extending Ability Feedforward neural networks Paper technology Electronic mail Helium Spline Convergence Weight Function Neural networks Liner Neural Networks
Content Type	Text
Resource Type	Article

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