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Proceedings Paper

Active random noise control using adaptive learning rate neural networks with an immune feedback law
Author(s): Minoru Sasaki; Takumi Kuribayashi; Satoshi Ito
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Paper Abstract

In this paper an active random noise control using adaptive learning rate neural networks with an immune feedback law is presented. The adaptive learning rate strategy increases the learning rate by a small constant if the current partial derivative of the objective function with respect to the weight and the exponential average of the previous derivatives have the same sign, otherwise the learning rate is decreased by a proportion of its value. The use of an adaptive learning rate attempts to keep the learning step size as large as possible without leading to oscillation. In the proposed method, because of the immune feedback law change a learning rate of the neural networks individually and adaptively, it is expected that a cost function minimize rapidly and training time is decreased. Numerical simulations and experiments of active random noise control with the transfer function of the error path will be performed, to validate the convergence properties of the adaptive learning rate Neural Networks with the immune feedback law. Control results show that adaptive learning rate Neural Networks control structure can outperform linear controllers and conventional neural network controller for the active random noise control.

Paper Details

Date Published: 2 May 2006
PDF: 6 pages
Proc. SPIE 6042, ICMIT 2005: Control Systems and Robotics, 60420O (2 May 2006); doi: 10.1117/12.664558
Show Author Affiliations
Minoru Sasaki, Gifu Univ. (Japan)
Takumi Kuribayashi, Gifu Univ. (Japan)
Satoshi Ito, Gifu Univ. (Japan)


Published in SPIE Proceedings Vol. 6042:
ICMIT 2005: Control Systems and Robotics
Yunlong Wei; Kil To Chong; Takayuki Takahashi; Shengping Liu; Zushu Li; Zhongwei Jiang; Jin Young Choi, Editor(s)

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