Systems identification with fuzzy spline wavelets

Fuzzy systems approximation by fuzzy spline wavelets

10.1117/12.205459 ◽

1995 ◽

Author(s):

Oded Z. Maimon ◽

Armin Shmilovici

Keyword(s):

Fuzzy Systems ◽

Spline Wavelets ◽

Fuzzy Spline

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On the solution of differential equations with fuzzy spline wavelets

Fuzzy Sets and Systems ◽

10.1016/s0165-0114(96)00285-0 ◽

1998 ◽

Vol 96 (1) ◽

pp. 77-99 ◽

Cited By ~ 5

Author(s):

Armin Shmilovici ◽

Oded Maimon

Keyword(s):

Differential Equations ◽

Spline Wavelets ◽

Fuzzy Spline

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Wavelet Transforms with a Compact Carrier on the Basis of Spline Wavelets

Telecommunications and Radio Engineering ◽

10.1615/telecomradeng.v66.i6.40 ◽

2007 ◽

Vol 66 (6) ◽

pp. 505-512

Author(s):

A. D. Kukharev ◽

Yu. S. Evstifeev ◽

V. G. Yakovlev

Keyword(s):

Wavelet Transforms ◽

Spline Wavelets

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A new generalized vector observation for discrete-time delay systems identification

2013 European Control Conference (ECC) ◽

10.23919/ecc.2013.6669539 ◽

2013 ◽

Cited By ~ 1

Author(s):

Saida Bedoui ◽

Majda Ltaief ◽

Kamel Abderrahim

Keyword(s):

Time Delay ◽

Discrete Time ◽

Delay Systems ◽

Time Delay Systems ◽

Discrete Time Delay ◽

Vector Observation ◽

Systems Identification

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CONTINUOUS-TIME FRACTIONAL ORDER LINEAR SYSTEMS IDENTIFICATION USING CHEBYSHEV WAVELET

EurasianUnionScientists ◽

10.31618/esu.2413-9335.2020.6.77.1002 ◽

2020 ◽

Vol 6 (8(77)) ◽

pp. 23-28

Author(s):

Shuen Wang ◽

Ying Wang ◽

Yinggan Tang

Keyword(s):

Linear Systems ◽

Fractional Order ◽

Continuous Time ◽

Fractional Integration ◽

Operational Matrices ◽

Computation Complexity ◽

Order Linear ◽

Fractional Integration Operator ◽

Systems Identification ◽

Chebyshev Wavelet

In this paper, the identification of continuous-time fractional order linear systems (FOLS) is investigated. In order to identify the differentiation or- ders as well as parameters and reduce the computation complexity, a novel identification method based on Chebyshev wavelet is proposed. Firstly, the Chebyshev wavelet operational matrices for fractional integration operator is derived. Then, the FOLS is converted to an algebraic equation by using the the Chebyshev wavelet operational matrices. Finally, the parameters and differentiation orders are estimated by minimizing the error between the output of real system and that of identified systems. Experimental results show the effectiveness of the proposed method.

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