New recursive-least-squares algorithms for nonlinear active control of sound and vibration using neural networks

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent alg...

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Vydáno v:IEEE transactions on neural networks Ročník 12; číslo 1; s. 135 - 147
Hlavní autor: Bouchard, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2001
Témata:
Active control
Actuators
Adjoints
Algorithms
Backpropagation algorithms
Computer simulation
Control systems
Convergence
Feedforward neural networks
Multi-layer neural network
Neural networks
Nonlinear control systems
Nonlinear filters
Nonlinearity
Vibration
Vibration control
ISSN:1045-9227
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Shrnutí:In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.
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ISSN:1045-9227
DOI:10.1109/72.896802