IIR model identification using batch-recursive adaptive simulated annealing algorithm
IIR model identification using batch-recursive adaptive simulated annealing algorithm
System identification using infinite-impulse-response (IIR) model is considered. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are preferred in order to avoid local minima. An efficient global optimisation method, called the adaptive simulated annealing (ASA), is adopted, and a new batch-recursive ASA algorithm is developed for on-line identification. Simulation study shows that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the ASA offers a viable tool to IIR model identification.
0 9533890 3 0
151-155
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
September 2000
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Chen, S.
(2000)
IIR model identification using batch-recursive adaptive simulated annealing algorithm.
Proceedings of 6th Annual Chinese Automation and Computer Science Conference in UK.
.
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Conference or Workshop Item
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Abstract
System identification using infinite-impulse-response (IIR) model is considered. Because the error surface of IIR filters is generally multi-modal, global optimisation techniques are preferred in order to avoid local minima. An efficient global optimisation method, called the adaptive simulated annealing (ASA), is adopted, and a new batch-recursive ASA algorithm is developed for on-line identification. Simulation study shows that the proposed approach is accurate and has a fast convergence rate, and the results obtained demonstrate that the ASA offers a viable tool to IIR model identification.
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Published date: September 2000
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Presented at 6th Annual Chinese Automation and Computer Science Conference in UK (Loughborough) , 23 September, 2000. Organisation: Chinese Automation and Computer Society in UK
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Proceedings of 6th Annual Chinese Automation and Computer Science Conference in UK, 2000-09-01
Organisations:
Southampton Wireless Group
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Local EPrints ID: 253952
URI: http://eprints.soton.ac.uk/id/eprint/253952
ISBN: 0 9533890 3 0
PURE UUID: e90707f8-f661-49ad-84c7-d9d8dce15438
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Date deposited: 10 Apr 2001
Last modified: 14 Mar 2024 05:29
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Author:
S. Chen
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