NDLI: Asymptotic minimization of the approximation error of competitive associative nets and its application to temperature control of RCA cleaning solutions

Content Provider	IEEE Xplore Digital Library
Author	Kurogi, S. Sakamoto, H. Nobutomo, H. Fuchikawa, Y. Nishida, T. Mimata, M. Itoh, K.
Copyright Year	2002
Description	Author affiliation: Kyushu Inst. of Technol., Kitakyushu, Japan (Kurogi, S.; Sakamoto, H.; Nobutomo, H.; Fuchikawa, Y.; Nishida, T.)
Abstract	The competitive associative net, called CAN2-2, is presented for learning to approximate time-varying dynamics of a plant in order to control the plant. Although the learning method has been shown effective in the previous studies, it uses the gradient method involving local minima problems. To overcome the problems, we here consider an asymptotic situation, where the number of units of the net is very large, and show that the mean square error of the CAN2-2 in approximating time-varying function decreases and is minimized as the number of units increases when the firing numbers of the units are equated. Next, we embed the condition for equating the firing numbers into the learning algorithm of the CAN2-2, and then examine the conventional model switching predictive controller using the modified CAN2-2 in temperature control of the RCA solutions for cleaning silicon wafers which expose the exothermic nonlinear and time-varying chemical reactions. The result confirms that the present method has better learning properties than the conventional one.
Sponsorship	Asia-Pacific Neural Network Assembly Singapore Neuroscience Assoc. SEAL & FSKD Conference Steering Committees IEEE Neural Networks Soc. Int. Neural Network Soc. Eur. Neural Network Soc. SPIE
Starting Page	1900
Ending Page	1904
File Size	284281
Page Count	5
File Format	PDF
ISBN	9810475241
DOI	10.1109/ICONIP.2002.1199004
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-18
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	Approximation error Temperature control Cleaning Firing Learning systems Gradient methods Mean square error methods Semiconductor device modeling Predictive models Silicon
Content Type	Text
Resource Type	Article

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