Synchronization for a Class of Fractional-order Linear Complex Networks via Impulsive Control

2018 ◽  
Vol 16 (6) ◽  
pp. 2839-2844 ◽  
Author(s):  
Na Liu ◽  
Jie Fang ◽  
Wei Deng ◽  
Zhen-Jun Wu ◽  
Guo-Qiang Ding
Keyword(s):  
Complex Networks ◽  
Fractional Order ◽  
Impulsive Control ◽  
Linear Complex ◽  
Order Linear

Chaotifying Stable Linear Complex Networks via Single Pinning Impulsive Strategy

2019 ◽  
Vol 29 (02) ◽  
pp. 1950024 ◽  
Author(s):  
Guang Ling ◽  
Zhi-Hong Guan ◽  
Jie Chen ◽  
Qiang Lai
Keyword(s):  
Lyapunov Exponent ◽  
Complex Networks ◽  
Impulsive Control ◽  
Largest Lyapunov Exponent ◽  
Continuous Systems ◽  
Linear Complex ◽  
Numerical Examples ◽  
Linear Networks ◽  
Pinning Impulsive Control ◽  
Stable Linear

Chaotification, aiming to make an original nonchaotic system chaotic, or enhancing an existing chaos, has attracted much attention recently. This paper investigates chaotification problem of stable linear complex networks, both discrete and continuous systems, via single pinning impulsive control. Under this scheme, the boundedness of these linear networks can be guaranteed, and the largest Lyapunov exponent of these controlled linear complex networks can be calculated in detail by theoretical analysis. Finally, two numerical examples are given to demonstrate the effectiveness of this strategy.

CONTINUOUS-TIME FRACTIONAL ORDER LINEAR SYSTEMS IDENTIFICATION USING CHEBYSHEV WAVELET

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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