Parameter identification for discrete memristive chaotic map using adaptive differential evolution algorithm

Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of works on the discrete memristor are devoted to the mathematical modeling and digital circuit implementation, but the research on its synchronization control has not received mu...

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Vydané v:Nonlinear dynamics Ročník 107; číslo 1; s. 1263 - 1275
Hlavní autori: Peng, Yuexi, He, Shaobo, Sun, Kehui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.01.2022
Springer Nature B.V
Predmet:
Adaptive algorithms
Algorithms
Automotive Engineering
Chaos theory
Classical Mechanics
Control
Digital electronics
Dynamical Systems
Engineering
Evolutionary algorithms
Evolutionary computation
Mathematical models
Mechanical Engineering
Memristors
Optimization
Original Paper
Parameter identification
Parameter modification
Sequences
Synchronism
Vibration
Discrete memristor
Chaotic map
Differential evolution algorithm
Parameter identification
Coexisting attractors
Return maps
ISSN:0924-090X, 1573-269X
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Shrnutí:Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of works on the discrete memristor are devoted to the mathematical modeling and digital circuit implementation, but the research on its synchronization control has not received much attention. This paper focuses on the parameter identification for the discrete memristive chaotic map, and a modified intelligent optimization algorithm named adaptive differential evolution algorithm is proposed. To deal with the complex behaviors of hyperchaos and coexisting attractors of the considered discrete memristive chaotic maps, the identification objective function adopts two special parts: time sequences and return maps. Numerical simulations demonstrate that the proposed algorithm has the best performance among six existing algorithms, and it can still accurately identify the parameters of the original system under noise interference.
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-021-06993-0