Neural network adaptive control for machining process based on generalised entropy square error and wavelet analysis
by XingYu Lai, ChunYan Yan, BangYan Ye
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 17, No. 3, 2009

Abstract: To improve convergent speed of the neural network for machining process control, Generalised Entropy Square Error (GESE) function is defined and its availability is proved theoretically. Combining information entropy and wavelet analysis with neural network, a neural network adaptive control system is presented. Replacing the mean square error criterion of back propagation algorithm with the GESE criterion, an adaptive control algorithm is proposed. The proposed system is then applied to the online control of the cutting force by searching adaptively wavelet base function and self-adjusting scale parameter, translation parameter of the wavelet and weights of the network. The designed system is of fast response and less overshoot, and the suggested algorithm can tune adaptively the feed rate online till achieving a constant cutting force approaching the reference force in varied cutting conditions, thus raising machining efficiency and protecting tool. Finally, simulation and experiment examples are also given to demonstrate the effectiveness of the proposed system and algorithm.

Online publication date: Sat, 21-Mar-2009

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Manufacturing Technology and Management (IJMTM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com