Deterministic System Identification Using RBF Networks | Zendy

Joilson Batista de Almeida Rego | Zendy; Allan Martins | Zendy; Evandro Costa | Zendy

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Deterministic System Identification Using RBF Networks

Author(s) -

Joilson Batista de Almeida Rego,

Allan Martins,

Evandro Costa

Publication year - 2014

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2014/432593

Subject(s) - radial basis function , nonlinear system , basis (linear algebra) , computer science , identification (biology) , control theory (sociology) , artificial intelligence , equilibrium point , artificial neural network , nonlinear system identification , system identification , control engineering , engineering , mathematics , data mining , control (management) , physics , geometry , botany , quantum mechanics , biology , measure (data warehouse)

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

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