The design of self-organizing evolved polynomial neural networks based on learnable evolution model 3
Author
Source
The International Arab Journal of Information Technology
Issue
Vol. 9, Issue 2 (31 Mar. 2012), pp.124-132, 9 p.
Publisher
Publication Date
2012-03-31
Country of Publication
Jordan
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Nowadays, the development of advanced techniques of system modeling has received much attention.
Polynomial Neural Network (PNN) is a GMDH-type algorithm (Group Method of Data Handling), which is one of the useful methods for modeling nonlinear systems but PNN performance depends strongly on the number of input variables and the order of polynomial which are determined by trial and error.
In this paper, we discuss a new design methodology for polynomial neural networks PNN in the framework of Learnable Evolution Model (LEM3).
LEM3 is a new approach to evolutionary computation, which employs machine learning to guide evolutionary processes.
LEM3 is obtained better performance in shorter time in comparing with other well-known methods.
Also, LEM3 appears to be particularly suitable for solving complex optimization problems in which the fitness evaluation function is time consuming.
In this paper, we use LEM3 to search between all possible values for the number of input variables and the order of polynomial.
Evolved PNN performance is obtained by two nonlinear systems.
The experimental part of the study involves two representative time series such as Box-Jenkins gas furnace process and the Dow Jones stock index.
American Psychological Association (APA)
Farzi, Said. 2012. The design of self-organizing evolved polynomial neural networks based on learnable evolution model 3. The International Arab Journal of Information Technology،Vol. 9, no. 2, pp.124-132.
https://search.emarefa.net/detail/BIM-292602
Modern Language Association (MLA)
Farzi, Said. The design of self-organizing evolved polynomial neural networks based on learnable evolution model 3. The International Arab Journal of Information Technology Vol. 9, no. 2 (Mar. 2012), pp.124-132.
https://search.emarefa.net/detail/BIM-292602
American Medical Association (AMA)
Farzi, Said. The design of self-organizing evolved polynomial neural networks based on learnable evolution model 3. The International Arab Journal of Information Technology. 2012. Vol. 9, no. 2, pp.124-132.
https://search.emarefa.net/detail/BIM-292602
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 131-132
Record ID
BIM-292602