Improving the Performance of Process Controllers Using a New Clustered Neural Network

Abstract:

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This paper presents first a newly developed clustered neural network, which incorporates self-organization capacity into the well-known common multilayer perceptron (MLP) architecture. With this addition, it is possible to reduce significantly overall memory degradation of the neuro-controller during on-line training. In the second part of the paper, this clustered multilayer perceptron (CMLP) network is applied and compared to the MLP through modeling and simulations of machining processes. Simulation results presented using machining data demonstrate that the CMLP possesses more powerful modeling capacity than the standard MLP, offers better adaptability to new operating conditions, and finally performs more reliably. During on-line training with machining data about 65% degradation of previously learned information can be observed in the MLP as opposed to only 11% for the CMLP. Finally, an adaptive control scheme intended for on-line optimization of the machining processes is presented. This scheme uses a feed forward CMLP inverse neuro-controller which learns off-line and on-line the relationships between process inputs and output under simulated perturbations (i.e., tool wear and non-homogeneous workpiece material properties). The first results using the CMLP inverse neuro-controller are promising

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Periodical:

Edited by:

Elwin Mao and Linli Xu

Pages:

273-277

DOI:

10.4028/www.scientific.net/AEF.1.273

Citation:

M. R. Soleymani Yazdi and M. Guillot, "Improving the Performance of Process Controllers Using a New Clustered Neural Network", Advanced Engineering Forum, Vol. 1, pp. 273-277, 2011

Online since:

September 2011

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