Deterministic System Identification Using RBF Networks

被引：5

作者：

de Almeida Rego, Joilson Batista ^{[1
,2
]}

Martins, Allan de Medeiros ^{[3
]}

Costa, Evandro de B. ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Norte, LACI DEE, Postgrad Program Elect & Comp Engn PPgEEC, BR-59078970 Natal, RN, Brazil

[2] Fed Univ Alagoas UFAL, Inst Comp Sci, BR-57072900 Maceio, AL, Brazil

[3] Univ Fed Rio Grande do Norte, Dept Elect Engn, BR-59078970 Natal, RN, Brazil

来源：

MATHEMATICAL PROBLEMS IN ENGINEERING | 2014年 / 2014卷

关键词：

FUNCTION NEURAL-NETWORKS; OPTIMIZATION;

D O I：

10.1155/2014/432593

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

This paper presents an artificial intelligence application using a nonconventional mathematical tool: the radial basis function (RBF) networks, aiming to identify the current plant of an induction motor or other nonlinear systems. Here, the objective is to present the RBF response to different nonlinear systems and analyze the obtained results. A RBF network is trained and simulated in order to obtain the dynamical solution with basin of attraction and equilibrium point for known and unknown system and establish a relationship between these dynamical systems and the RBF response. On the basis of several examples, the results indicating the effectiveness of this approach are demonstrated.

引用

页数：10

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