New Recursive-Least-Squares Algorithms for Nonlinear Active Control of Sound and Vibration using Neural Networks  

Oleh:

Bouchard, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 12 no. 1 (2001), page 135-147.
Topik: algorithms; recursive - least - squares; algorithms; non linear; vibration; neural networks

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.5 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In recent years, a few articles describing the use of neural networks for non linear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a non linear 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 non linear 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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