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 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.
More information can be found in
Erik Cuevas, Jorge Gálvez, Salvador Hinojosa, Omar Avalos, Daniel Zaldívar, and Marco Pérez-Cisneros, “A Comparison of Evolutionary Computation Techniques for IIR Model Identification,” Journal of Applied Mathematics Volume 2014 (2014), Article ID 827206, 8 pages.
http://www.hindawi.com/journals/jam/2014/827206/
Cite As
Erik (2024). A Comparison of Evolutionary Computation Techniques for IIR Model Identification (https://www.mathworks.com/matlabcentral/fileexchange/48652-a-comparison-of-evolutionary-computation-techniques-for-iir-model-identification), MATLAB Central File Exchange. Retrieved .
MATLAB Release Compatibility
Platform Compatibility
Windows macOS LinuxTags
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!Discover Live Editor
Create scripts with code, output, and formatted text in a single executable document.
ABC/ABC/ABC/
Cuckoo Algorithm/
EMO/
FPA/
PSO/First Example/
PSO/Fourth Example/
PSO/Second Example/
Version | Published | Release Notes | |
---|---|---|---|
1.4 | Description was updated |
|
|
1.3.0.0 | the description was updated |
|
|
1.2.0.0 | The article link has been updated |
|
|
1.1.0.0 | Description was expended |
|
|
1.0.0.0 |
|