Filtered reference and filtered error LMS algorithms for adaptive feedforward control

Mechanical Systems and Signal Processing

Volumn 12, Issue 6, 1998, Pages 769-781

  Elliott, S J ^a  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; COMPUTATIONAL METHODS; ERROR ANALYSIS; FEEDFORWARD NEURAL NETWORKS; FIR FILTERS; LEAST SQUARES APPROXIMATIONS; LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; MULTIVARIABLE CONTROL SYSTEMS;

FILTERED ERROR; FILTERED REFERENCE; MULTIPLE-INPUT MULTIPLE-OUTPUT (MIMO) LINEAR SYSTEMS;

ADAPTIVE CONTROL SYSTEMS;

EID: 0032202179     PISSN: 08883270     EISSN: None     Source Type: Journal    
DOI: 10.1006/mssp.1998.0178     Document Type: Article

References (17)

1. P. A. NELSON and S. J. ELLIOTT 1992 Active Control of Sound. Academic Press.
2. C. R. FULLER, S. J. ELLIOTT and P. A. NELSON 1996 Active Control of Vibration. Academic Press.
3. B. WIDROW and E. WALACH 1996 Adaptive Inverse Control. Prentice-Hall.
4. W. A. SETHARES, B. D. O. ANDERSON and C. R. JOHNSON 1989 Mathematics of Control, Signals and Systems 2, 381-403. Adaptive algorithms with filtered regressor and filtered error.
5. D. R. MORGAN 1980 IEEE Transactions Acoustic, Speech and Signal Processing ASSP-28, 545-467. An analysis of multiple correlation loops with a filter in the auxiliary path.
6. J. C. BURGESS 1981 Journal of the Acoustical Society of America 70, 715-726. Active adaptive sound control in a duct: a computer simulation.
7. B. WIDROW, D. SCHUR and S. SHAFFER 1981 Proceedings of the 15th ASILOMAR Conference on Circuits, Systems and Computers, 185-195. On adaptive inverse control.
8. E. A. WAN 1993 PhD Thesis, Stanford University. Finite impulse response neural network with applications in time series prediction.
9. E. A. WAN 1996 Proceedings of ICASSP 96. Adjoint LMS: an efficient alternative to the filtered-x LMS and multiple error LMS algorithms.
10. W. REN and P. R. KUMAR 1989 Proceedings of Inter-Noise 89, 435-440. Adaptive active noise control: structures, algorithms and convergence analysis.
11. M. MORARI and E. ZAfiRIOU 1989 Robust Process Control. Prentice Hall.
12. S. J. ELLIOTT, I. M. STOTHERS and P. A. NELSON 1987 IEEE Transactions Acoustics, Speech and Signal Processing ASSP-35, 1423-1434. A multiple error LMS algorithm and its application to the active control of sound and vibration.
13. S. R. POPOVICH 1994 Proceedings of Inter-Noise 93, 1229-1233. A simplified parameter update for identification of multiple input, multiple output system.
14. F. BEAUFAYS and E. A. WAN 1994 Neural Computation 6, 296-306. Relating real-time back propagation and back propagation through time: An application of flow graph interreciprocity.
15. S. MARCOS, O. MACCHI, C. VIGNAT, G. DREYFUS, L. PERSONNAZ and P. ROUSSEL-RAGOT 1992 International Journal of Circuit Theory and Applications 20, 159-200. A unified framework for gradient algorithms used for filter adaptation and neural network training.
16. R. J. WILLIAMS and D. ZIPSER 1989 Neural Computation 1, 270-280. A learning algorithm for continually running fully recurrent neural networks.
17. D. E. RUMELHART, G. E. HINTON and R. J. WILLIAMS 1986 In Parallel distributed processing: Vol. 1., Foundations MIT Press D. Rumelhart and J. McClelland (eds). Learning internal representations by error propagation.