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Title: An enhanced approach for parameter estimation: using immune dynamic learning swarm optimization based on multicore architecture
Authors: Liu, Zhao-Hua
Li, Xiao-Hua
Zhang, Hong-Qiang
Wu, Liang-Hong
Liu, Kan
Keywords: Parameter estimation
Multicore processing
Immune system
Heuristic algorithms
Biological system modeling
Mathematical model
Issue Date: 2016
Publisher: © IEEE
Citation: LIU, Z.-H. ... et al, 2016. An enhanced approach for parameter estimation: using immune dynamic learning swarm optimization based on multicore architecture. IEEE Systems, Man, and Cybernetics Magazine, 2 (1), pp. 26-33.
Abstract: The identification of physical parameters is crucial for control system designs, condition monitoring and fault diagnosis of industrial drive systems. The article brings multicore architecture based parallel computing technology and bioinspired intelligent optimisation algorithm insight into designing for system parameter estimation models.models. In this study, a parallel implementation using an immune-cooperative dynamic learning particle swarm optimization (PSO) algorithm with multicore computation architectures is presented for permanent magnet synchronous machine (PMSM) parameter estimations. Three novel strategies are discussed, all with the purpose of enhancing the dynamic response and fast convergence performance of the designed parameter estimator. The strategies include a dynamic velocity modification strategy, an immune-memory-based searched information preservation mechanism, and an immune-network-based learning operator for PSO. Finally, a proposed method is applied to the parameter estimations of PMSMs as well as parallel running on multicore central processing units (CPU).
Description: This paper is in closed access.
Sponsor: This work was supported in part by the National Natural Science Foundation of China under Grant (51374107, 61503134, 51577057, 61573299), the China Postdoctoral Science Foundation funded project under Grant (2013M540628, 2014T70767), and the Hunan Provincial Education Department outstanding youth project under Grant (15B087).
Version: Published
DOI: 10.1109/MSMC.2015.2472915
URI: https://dspace.lboro.ac.uk/2134/22599
Publisher Link: http://dx.doi.org/10.1109/MSMC.2015.2472915
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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