Improved training of neural networks for the nonlinear active control of sound and vibration

Active control of sound and vibration has been the subject of a lot of research, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in...

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Bibliographic Details
Published in:IEEE transactions on neural networks Vol. 10; no. 2; pp. 391 - 401
Main Authors: Bouchard, M., Paillard, B., Chon Tan Le Dinh
Format: Journal Article
Language:English
Published: New York, NY IEEE 01.03.1999
Institute of Electrical and Electronics Engineers
Subjects:
Acoustic sensors
Active control
Actuators
Algorithms
Applied sciences
Backpropagation algorithms
Control systems
Controllers
Ducts
Electric, optical and optoelectronic circuits
Electronics
Exact sciences and technology
Interference
Neural networks
Nonlinear control systems
Nonlinearity
Sensor systems
Sound
Training
Vibration control
Backpropagation
Least squares problem
Non linear control
Vibration control
Neural network
Sound source
Learning algorithm
Recursive algorithm
Active control
ISSN:1045-9227
Online Access:Get full text
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Summary:Active control of sound and vibration has been the subject of a lot of research, and examples of applications are now numerous. However, few practical implementations of nonlinear active controllers have been realized. Nonlinear active controllers may be required in cases where the actuators used in active control systems exhibit nonlinear characteristics, or in cases when the structure to be controlled exhibits a nonlinear behavior. A multilayer perceptron neural-network based control structure was previously introduced as a nonlinear active controller, with a training algorithm based on an extended backpropagation scheme. This paper introduces new heuristical training algorithms for the same neural-network control structure. The objective is to develop new algorithms with faster convergence speed and/or lower computational loads. Experimental results of active sound control using a nonlinear actuator with linear and nonlinear controllers are presented. The results show that some of the new algorithms can greatly improve the learning rate of the neural-network control structure, and that for the considered experimental setup a neural-network controller can outperform linear controllers.
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ISSN:1045-9227
DOI:10.1109/72.750568