Minimum entropy control for stochastic systems based on the wavelet neural networks

Authors:
Chengzhi Yang
Affiliations:
Kunming University of Science and Technology, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 2
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An error-entropy minimization algorithm for supervised training ofnonlinear adaptive systems

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Adaptive blind deconvolution of linear channels using Renyi's entropy with Parzen window estimation

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Abstract

The main idea of this paper is to characterize the uncertainties of control system base upon entropy concept. The wavelet neural networks is used to approach the nonlinear system through minimizing Renyi’s entropy criterion of the system estimated error, and the controller design is based upon minimizing Renyi’s entropy criterion of the system tracking errors. An illustrative example is utilized to demonstrate the effectiveness of this control solution, and satisfactory results have been obtained.