Generalized PSO Algorithm - an Application to Lorenz System Identification by Means of Neural-Networks

被引：0
作者：
Rapaic, Milan R. [1 ]
Kanovic, Zeljko [1 ]
Jelicic, Zoran D. [1 ]
Petrovacki, Dusan [1 ]
机构：
[1] Fac Tech Sci Novi Sad, Novi Sad, Serbia
来源：
NEUREL 2008: NINTH SYMPOSIUM ON NEURAL NETWORK APPLICATIONS IN ELECTRICAL ENGINEERING, PROCEEDINGS | 2008年
关键词：
Particle Swarm Optimization; Radial Basis Function Neural Networks; System Identification;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a new, generalized PSO (GPSO) algorithm is presented and analyzed, both theoretically and empirically. The new optimizer enables direct control over the properties of the search process. In addition, PSO is addressed in conceptually different manner, revealing further aspects of the algorithm behavior. GPSO is applied for training radial basis function neural network (RBF-NN) to identify dynamics of a nonlinear system. The target system is chosen to be of Lorenz type, known for its complex, chaotic behavior. Results presented in this paper clearly demonstrate effectiveness of the proposed algorithm.
引用
收藏
页码：30 / 34
页数：5
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