INTELLIGENT PARTICLE SWARM CLASSIFIER

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ZAHIRI S.H.; SEYEDIN S.A.R.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE) Year:2005 | Volume:4 | Issue:1 Page(s): 63-70

Download Full-Text

View:

371

Download:

194

Cites:

Information Journal Paper

Title

INTELLIGENT PARTICLE SWARM CLASSIFIER

Author(s)

ZAHIRI S.H. | SEYEDIN S.A.R. | Issue Writer Certificate

Keywords

DECISION HYPERPLANES

FUZZY CONTROLLER

PARTICLE SWARM OPTIMIZATION

PATTERN RECOGNITION

Abstract

An Intelligent Particle Swarm classifier (IPSclassifier) is proposed in this paper. This classifier is described for finding the DECISION HYPERPLANES to classify patterns of different classes in the feature space using PARTICLE SWARM OPTIMIZATION (PSO) algorithm. An intelligent FUZZY CONTROLLER is designed to improve the performance and efficiency of proposed swarm intelligence based classifier by adapting three important parameters of PSO (i.e., swarm size, neighborhood size, and constriction coefficient). Three PATTERN RECOGNITION problems with different feature vector dimensions were used to demonstrate the effectiveness of the proposed classifier. They are the Iris data classification, the Wine data classification, and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers.

Cites

No record.

References

Cite

APA: Copy

ZAHIRI, S.H., & SEYEDIN, S.A.R.. (2005). INTELLIGENT PARTICLE SWARM CLASSIFIER. IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE), 4(1), 63-70. SID. https://sid.ir/paper/283352/en

Vancouver: Copy

ZAHIRI S.H., SEYEDIN S.A.R.. INTELLIGENT PARTICLE SWARM CLASSIFIER. IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE)[Internet]. 2005;4(1):63-70. Available from: https://sid.ir/paper/283352/en

IEEE: Copy

S.H. ZAHIRI, and S.A.R. SEYEDIN, “INTELLIGENT PARTICLE SWARM CLASSIFIER,” IRANIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING (IJECE), vol. 4, no. 1, pp. 63–70, 2005, [Online]. Available: https://sid.ir/paper/283352/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops