| dc.contributor.author |
Patwardhan, Apoorv P. |
|
| dc.contributor.author |
Patidar, Rohan |
|
| dc.contributor.author |
George, Nithin V. |
|
| dc.date.accessioned |
2014-06-26T05:14:07Z |
|
| dc.date.available |
2014-06-26T05:14:07Z |
|
| dc.date.issued |
2014-09 |
|
| dc.identifier.citation |
Patwardhan, Apoorv; Patidar, Rohan and George, Nithin V., "On a cuckoo search optimization approach towards feedback system identification", Digital Signal Processing, DOI: 10.1016/j.dsp.2014.05.008, 2014. |
en_US |
| dc.identifier.issn |
1051-2004 |
|
| dc.identifier.uri |
http://dx.doi.org/10.1016/j.dsp.2014.05.008 |
|
| dc.identifier.uri |
https://repository.iitgn.ac.in/handle/123456789/1317 |
|
| dc.description.abstract |
This paper presents a cuckoo search algorithm (CSA) based adaptive infinite impulse response (IIR) system identification scheme. The proposed scheme prevents the local minima problem encountered in conventional IIR modeling mechanisms. The performance of the new method has been compared with that obtained by other evolutionary computing algorithms like genetic algorithm (GA) and particle swarm optimization (PSO). The superior system identification capability of the proposed scheme is evident from the results obtained through an exhaustive simulation study. |
en_US |
| dc.description.statementofresponsibility |
by Apoorv Patwardhan, Rohan Patidar and Nithin V. George |
|
| dc.format.extent |
vol. 32, pp. 156-163 |
|
| dc.language.iso |
en |
en_US |
| dc.publisher |
Elsevier |
en_US |
| dc.subject |
Cuckoo search algorithm |
en_US |
| dc.subject |
Genetic algorithm |
en_US |
| dc.subject |
IIR system |
en_US |
| dc.subject |
Particle swarm optimization algorithm |
en_US |
| dc.subject |
System identification |
en_US |
| dc.title |
On a cuckoo search optimization approach towards feedback system identification |
en_US |
| dc.type |
Article |
en_US |
| dc.relation.journal |
Digital Signal Processing |
|