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

• Published in: Nonlinear dynamics Vol. 107; no. 1; pp. 1263 - 1275
• Main Authors: Peng, Yuexi, He, Shaobo, Sun, Kehui
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 2022; Springer Nature B.V
• Subjects: 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
• DOI: 10.1007/s11071-021-06993-0

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.