Blind Identification of Nonlinear MIMO System Using Differential Evolution Techniques and Performance Analysis of Its Variants

The present work deals with the nonlinear multiple input multiple output (MIMO) system identification exploring the use of evolutionary computing techniques such as Differential Evolution. The conventionally used standard derivative based identification schemes does not work satisfactorily for nonli...

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Bibliographic Details
Published in2015 International Conference on Computational Intelligence and Networks pp. 63 - 67
Main Authors Swayamsiddha, Swati, Behera, Sabyasachi, Thethi, H. Pal
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2015
Subjects
Adaptation models
Computation
crossover
Derivatives
differential evolution
Evolution
Evolutionary
Intelligence
Mathematical model
MIMO
mutation
nonlinear system identification
Nonlinear systems
Nonlinearity
Optimization
Settling
Signal processing algorithms
variants of DE
Vectors
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Summary:The present work deals with the nonlinear multiple input multiple output (MIMO) system identification exploring the use of evolutionary computing techniques such as Differential Evolution. The conventionally used standard derivative based identification schemes does not work satisfactorily for nonlinear MIMO systems, which is due to premature settling of weights but the proposed update algorithm works better preventing the premature settling of the model parameters. Simultaneously, the performance comparison of different variants of DE has been demonstrated which reveals the best mutant of DE family that can be implemented into prescribed identification process through the real world applications.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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content type line 23
SourceType-Conference Papers & Proceedings-2
ISBN:1479975486
9781479975488
ISSN:2375-5822
DOI:10.1109/CINE.2015.22