A hybrid learning algorithm for evolving Flexible Beta Basis Function Neural Tree Model

NEUROCOMPUTING

卷:117

Article

Bouaziz, Souhir¹  Dhahri, Habib¹  Alimi, Adel M.¹  Abraham, Ajith² 

[1] Univ Sfax, Natl Sch Engineers ENIS, Res Grp Intelligent Machines REGIM, Sfax 3038, Tunisia

[2] Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Ostrava, Czech Republic

关键词: Flexible Beta Basis Function Neural Tree Model;    Extended genetic programming;    Opposite-based particle swarm optimization algorithm;    Time-series forecasting;    Control system;   

DOI  :  10.1016/j.neucom.2013.01.024

来源: Elsevier

【 摘 要 】

In this paper, a tree-based encoding method is introduced to represent the Beta basis function neural network. The proposed model called Flexible Beta Basis Function Neural Tree (FBBFNT) can be created and optimized based on the predefined Beta operator sets. A hybrid learning algorithm is used to evolving FBBFNT Model: the structure is developed using the Extended Genetic Programming (EGP) and the Beta parameters and connected weights are optimized by the Opposite-based Particle Swarm Optimization algorithm (OPSO). The performance of the proposed method is evaluated for benchmark problems drawn from control system and time series prediction area and is compared with those of related methods. (C) 2013 Elsevier B.V. All rights reserved.

【 授权许可】

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