Research on Algorithm Optimization of Hidden Units Data Centre of RBF Neural Network

Jing Ying Zhao; Hai Guo; Xiao Niu Li

doi:10.4028/www.scientific.net/AMR.831.486

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 831Research on Algorithm Optimization of Hidden Units...

Research on Algorithm Optimization of Hidden Units Data Centre of RBF Neural Network

Abstract:

Common algorithms of selecting hidden unit data center in RBF neural networks were first discussed in this essay, i.e. k-means algorithm, subtractive clustering algorithm and orthogonal least squares. Meanwhile, a hybrid algorithm mixed of k-means algorithm and particle swarm optimization algorithm was put forward. The algorithm used the position of the particles in particle swarm optimization algorithm to help deal with the defects of local clusters resulted from k-means algorithm and to make optimization with the optimal fitness of k-means particle swarm with the aim to make the final optimal fitness better satisfy the requirements.

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Periodical:

Advanced Materials Research (Volume 831)

Pages:

486-489

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.831.486

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Online since:

December 2013

Authors:

Jing Ying Zhao, Hai Guo*, Xiao Niu Li

Keywords:

K-Mean, Orthogonal Least Squares, Particle Swarm Optimization Algorithm, RBF Radial Basis Neural Network, Subtractive Clustering Method

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