scholarly journals A new parameter identification method of a dual‐rotor flux‐modulation machine based on an adaptive differential evolution algorithm

2021 ◽  
Author(s):  
Yuan Mao ◽  
Shuangxia Niu ◽  
Yun Yang
Keyword(s):  
Differential Evolution ◽  
Parameter Identification ◽  
Differential Evolution Algorithm ◽  
Identification Method ◽  
Adaptive Differential Evolution ◽  
Flux Modulation ◽  
Rotor Flux ◽  
Evolution Algorithm

scholarly journals Sequential Parameter Identification of Fractional-Order Duffing System Based on Differential Evolution Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Li Lai ◽  
Yuan-Dong Ji ◽  
Su-Chuan Zhong ◽  
Lu Zhang
Keyword(s):  
Differential Evolution ◽  
Parameter Identification ◽  
Fractional Order ◽  
Dynamic Properties ◽  
Differential Evolution Algorithm ◽  
Duffing System ◽  
Identification Method ◽  
System A ◽  
Evolution Algorithm ◽  
Search Capability

Using the dynamic properties of fractional-order Duffing system, a sequential parameter identification method based on differential evolution optimization algorithm is proposed for the fractional-order Duffing system. Due to the step by step parameter identification method, the dimension of parameter identification of each step is greatly reduced and the search capability of the differential evolution algorithm has been greatly improved. The simulation results show that the proposed method has higher convergence reliability and accuracy of identification and also has high robustness in the presence of measurement noise.

scholarly journals Parameter Identification for Discrete Memristive Chaotic Map using Adaptive Differential Evolution Algorithm

2021 ◽  
Author(s):  
Yuexi Peng ◽  
Shaobo He ◽  
Kehui Sun
Keyword(s):  
Differential Evolution ◽  
Parameter Identification ◽  
Chaotic Map ◽  
Differential Evolution Algorithm ◽  
Original System ◽  
Noise Interference ◽  
Return Maps ◽  
Adaptive Differential Evolution ◽  
Intelligent Optimization Algorithm ◽  
Evolution Algorithm

Abstract Since the concept of discrete memristor was proposed, more and more scholars began to study this topic. At present, most of the works on the discrete memristor are devoted to the mathematical modeling and 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 the six existing algorithms, and it can still accurately identify the parameters of the original system under noise interference.

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