Fuzzy Kalman Filter Modeling Based on Evolving Clustering of Experimental Data

被引：0

作者：

Pires, Danubia Soares ^{[1
]}

de Oliveira Serra, Ginalber Luiz ^{[1
]}

机构：

[1] Fed Inst Maranhao, Dept Electroelect, Lab Computat Intelligence Appl Technol, Sao Luis, Brazil

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2018年

关键词：

IDENTIFICATION; SYSTEM;

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：

A methodology to system identification based on Evolving Fuzzy Kalman Filter, is proposed in this paper. The mathematical formulation using an evolving Takagi-Sugeno (TS) structure is presented: the offline Gustafson Kessel (GK) Algorithm is used to a window of initial data set; after that, an evolving GK algorithm estimate the antecedent parameters. A fuzzy version OKID (Observer/Kalman Filter Identification) algorithm is formulated to obtain the state matrix, the input influence matrix, the output influence matrix, the direct transmission matrix, and the Kalman gain matrix, recursively, composing the consequent parameters. Computational results from the estimation of parameters of nonlinear dynamic system show the efficiency and applicability of the proposed methodology.

引用

页数：6

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