A Novel Knowledge Diffusion Efficiency Prediction Arithmetic in Equipment Manufacturing Industry Based on Simulated Annealing Arithmetic

Xu Sheng Chen; Wen Jun Yue; Hong Qi Wang

doi:10.4028/www.scientific.net/AMM.441.768

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 441A Novel Knowledge Diffusion Efficiency Prediction...

A Novel Knowledge Diffusion Efficiency Prediction Arithmetic in Equipment Manufacturing Industry Based on Simulated Annealing Arithmetic

Abstract:

A novel knowledge diffusion efficiency prediction arithmetic in equipment manufacturing industry in China was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the precision with 10^-4 error, also the learning speed is quick and predictions are ideal. Trainings have been done with other networks in comparison. Back-propagation learning algorithm network does not converge until 2000 iterative procedure, and exactness design RBFNN is time-consuming and has big error. The arithmetic can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

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Periodical:

Applied Mechanics and Materials (Volume 441)

Pages:

768-771

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.441.768

Citation:

Cite this paper

Online since:

December 2013

Authors:

Xu Sheng Chen, Wen Jun Yue, Hong Qi Wang

Keywords:

Back-Propagation Learning Algorithm Network, Diffusion Efficiency Prediction Arithmetic, Equipment Manufacturing Industry in China, Exactness Design Radial Basis Function Neural Network, Radial Basis Function Neural Network, Simulated Annealing Arithmetic

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