A Comparison of Evolutionary Computation Techniques for IIR Model Identification
System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models sinc...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2014/827206 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841524459124555776 |
|---|---|
| author | Erik Cuevas Jorge Gálvez Salvador Hinojosa Omar Avalos Daniel Zaldívar Marco Pérez-Cisneros |
| author_facet | Erik Cuevas Jorge Gálvez Salvador Hinojosa Omar Avalos Daniel Zaldívar Marco Pérez-Cisneros |
| author_sort | Erik Cuevas |
| collection | DOAJ |
| description | System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated. |
| format | Article |
| id | doaj-art-5ec49d9f167e403f98a6d278dc81fc29 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-5ec49d9f167e403f98a6d278dc81fc292025-02-03T05:53:11ZengWileyJournal of Applied Mathematics1110-757X1687-00422014-01-01201410.1155/2014/827206827206A Comparison of Evolutionary Computation Techniques for IIR Model IdentificationErik Cuevas0Jorge Gálvez1Salvador Hinojosa2Omar Avalos3Daniel Zaldívar4Marco Pérez-Cisneros5Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, MexicoDepartamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, MexicoDepartamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, MexicoDepartamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, MexicoDepartamento de Electrónica, Universidad de Guadalajara, CUCEI, Avenida Revolución 1500, 44430 Guadalajara, JAL, MexicoCUTONALA, Avenida Nuevo Periférico 555, Ejido San José Tateposco, 48525 Tonalá, JAL, MexicoSystem identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.http://dx.doi.org/10.1155/2014/827206 |
| spellingShingle | Erik Cuevas Jorge Gálvez Salvador Hinojosa Omar Avalos Daniel Zaldívar Marco Pérez-Cisneros A Comparison of Evolutionary Computation Techniques for IIR Model Identification Journal of Applied Mathematics |
| title | A Comparison of Evolutionary Computation Techniques for IIR Model Identification |
| title_full | A Comparison of Evolutionary Computation Techniques for IIR Model Identification |
| title_fullStr | A Comparison of Evolutionary Computation Techniques for IIR Model Identification |
| title_full_unstemmed | A Comparison of Evolutionary Computation Techniques for IIR Model Identification |
| title_short | A Comparison of Evolutionary Computation Techniques for IIR Model Identification |
| title_sort | comparison of evolutionary computation techniques for iir model identification |
| url | http://dx.doi.org/10.1155/2014/827206 |
| work_keys_str_mv | AT erikcuevas acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT jorgegalvez acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT salvadorhinojosa acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT omaravalos acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT danielzaldivar acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT marcoperezcisneros acomparisonofevolutionarycomputationtechniquesforiirmodelidentification AT erikcuevas comparisonofevolutionarycomputationtechniquesforiirmodelidentification AT jorgegalvez comparisonofevolutionarycomputationtechniquesforiirmodelidentification AT salvadorhinojosa comparisonofevolutionarycomputationtechniquesforiirmodelidentification AT omaravalos comparisonofevolutionarycomputationtechniquesforiirmodelidentification AT danielzaldivar comparisonofevolutionarycomputationtechniquesforiirmodelidentification AT marcoperezcisneros comparisonofevolutionarycomputationtechniquesforiirmodelidentification |