Equipment Manufacturing Industry Knowledge Chain Efficiency Prediction Algorithm Based on Improved RBFNN

Abstract:

A new knowledge chain efficiency prediction arithmetic in equipment manufacturing industry in China was proposed, Radial basis function neural network (RBFNN) was designed, and initial temperature numerical calculation 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 2400 iterative procedure, and Efficiency design Radial basis function neural network is time-consuming and has big error. The arithmetic in paper can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

• Periodical: Applied Mechanics and Materials (Volume 441)
• Pages: 776-779
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.441.776
• Online since: December 2013
• Authors: Xu Sheng Chen, Chen Peng Xu, Hong Qi Wang
• Keywords: Back-Propagation Learning Algorithm Network, Efficiency Design Radial Basis Function Neural Network, Equipment Manufacturing Industry in China, Initial Temperature Numerical Calculation Arithmetic, Knowledge Chain Efficiency Prediction Arithmetic, Radial Basis Function Neural Network

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