Wavelet OHIF Elman neural network model and its predictive control of processing quality

Guixiong Liu; Jie Yang

doi:10.1117/12.889125

26 May 2011 Wavelet OHIF Elman neural network model and its predictive control of processing quality

Guixiong Liu, Jie Yang

Proceedings Volume 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering; 79973O (2011) https://doi.org/10.1117/12.889125
Event: Fourth International Seminar on Modern Cutting and Measuring Engineering, 2010, Beijing, China

Abstract

There are some difficulties in build the process quality prediction model based on Elman neural network. The traditional Sigmoid activation function often used in the hidden layer, while it is difficult to establish a quantitative relationship between the network size and resolution scale, therefore, a wavelet OHIF Elman neural network model is proposed in this paper, which full use of the neural network weights of the linear distribution and learning convex objective function, so it can avoid the local optimal nonlinear optimization problems. Simulation results show that the wavelet Elman OHIF Elman network decreased 12.9 percent compared with OHIF Elman network which used the sigmoid activation function in hidden layer.

Citation Download Citation

Guixiong Liu and Jie Yang "Wavelet OHIF Elman neural network model and its predictive control of processing quality", Proc. SPIE 7997, Fourth International Seminar on Modern Cutting and Measurement Engineering, 79973O (26 May 2011); https://doi.org/10.1117/12.889125

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