Evolving flexible beta basis function neural tree for nonlinear systems

Author

Bouaziz, Souhir ; Alimi, Adel M. ; Abraham, Ajith

Author_Institution

Res. Group on Intell. Machines (REGIM), Univ. of Sfax, Sfax, Tunisia

fYear

2013

fDate

4-9 Aug. 2013

Firstpage

1

Lastpage

8

Abstract

In this paper, a new evolving artificial neural network using evolutionary computation is introduced. Based on the pre-defined Beta operator sets, this model called Flexible Beta Basis Function Neural Tree (FBBFNT), can be created and learned. The structure is developed using the Extended Immune Programming (EIP). The Beta parameters and connected weights are optimized using the Hybrid Bacterial Foraging Optimization algorithm. The performance of the proposed method is evaluated for nonlinear systems and compared with those of related methods.

Keywords

evolutionary computation; neural nets; trees (mathematics); EIP; FBBFNT; artificial neural network; beta operator sets; beta parameters; connected weights; evolutionary computation; extended immune programming; flexible beta basis function neural tree; hybrid bacterial foraging optimization algorithm; nonlinear systems; Artificial neural networks; Immune system; Microorganisms; Optimization; Programming; Sociology; Statistics; Extended Immune Programming; Flexible Beta Basis Function Neural Tree; Hybrid Bacterial Foraging Optimization algorithm; nonlinear systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2013 International Joint Conference on

Conference_Location

Dallas, TX

ISSN

2161-4393

Print_ISBN

978-1-4673-6128-6

Type

conf

DOI

10.1109/IJCNN.2013.6706992

Filename

6706992