The RBF Neural Network Learning Algorithm Based on Entropy Clustering and Competitive

Shao Yun Song; Mao Luo

doi:10.4028/www.scientific.net/AMM.687-691.1633

Paper Titles

The Growth of a Cosine Function Characterized by its Cogenerator
p.1616

The Application of MATLAB Simulation Technology on Evolutionary Game Analysis
p.1619

Scheme Automatic Generation Based on Full Life Cycle Assessment Gene Model of Structure
p.1622

Realization of RBF Neural Network with Local and Semi-Local Transfer Function Approximation and Classification
p.1628

The RBF Neural Network Learning Algorithm Based on Entropy Clustering and Competitive
p.1633

A Web Service Composition Algorithm Based on Graph Search and Semantic Web
p.1637

An Application of AHP and FAHP on the Model Prediction
p.1641

Resource Scheduling Optimization Model in Cloud Computing Environment
p.1645

Research of Dimensionality Reduction for Fatigue Stress Concentration Factor Based on SVM by Linear Kernel
p.1649

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691The RBF Neural Network Learning Algorithm Based on...

The RBF Neural Network Learning Algorithm Based on Entropy Clustering and Competitive

Abstract:

Research and analysis of RBF neural network structure and characteristics. Find out its shortcomings and propose an improved method for the deficiencies, then created a neural network model for using entropy-based clustering and competitive learning algorithm. Using MATLAB simulation tools for model simulation, confirmed the entropy clustering and competitive learning algorithm of FBF prediction neural network have high precision and generalization ability of stronger character.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1633-1636

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1633

Citation:

Cite this paper

Online since:

November 2014

Authors:

Shao Yun Song*, Mao Luo

Keywords:

Competitive Learning, Entropy Clustering, MATALB Simulation, RBF Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Li Bo, Wang Xiufeng. The new [J] algorithm to determine the radial basis function network structure, Journal ofmanagement science, 1999, second, third, 22-27.

Google Scholar

[2] Lv Jianhong, Chen Jianqin, et al. Research on [J]nonlinear models for thermal processes based on fuzzyrules, China. Proceedings of the CSEE, 2002 The twenty-second volume eleventh issue, 132-137.

Google Scholar

[3] Huang Hongtao. A two-way choice of Gaussian function and radial basis function neural network algorithm based on[J], computer science, Chongqing, 2007; 134th, seventeenth, 211-213.

Google Scholar

[4] Yan Pingfan, Zhang Changshui. Artificial neural network and simulated evolution calculation [M]. Beijing: Tsinghua University press, (2003).

Google Scholar

[5] Li Renhou. Intelligent control theory and method [M]. Xi'an: Xi'an Jiao Tong University press, (1994).

Google Scholar