2019
DOI: 10.1109/access.2019.2905247
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A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM

Abstract: The flux-linkage characteristics of bearingless induction motors (BIMs) are nonlinear, and the models established by the general analytical method cannot accurately reflect the actual characteristics of BIMs. Thus, a novel method for nonlinear modeling of BIM flux linkage is proposed in this paper. The main objective of this method is to improve the accuracy of the flux linkage model based on the least square support vector machine (LSSVM) technique by applying the gray wolf optimization (GWO) algorithm to det… Show more

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Cited by 29 publications
(14 citation statements)
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“…The GWO algorithm is a swarm intelligence algorithm proposed by [30] with good self-organizing learning ability, simple parameters, easy implementation, and good global searchability [31], [32]. And by comparing with other four famous meta-heuristic algorithms (PSO algorithm, gravity search algorithm, differential evolution algorithm and fast evolutionary programing algorithm) on 29 test functions, the simulation test proves its superiority.…”
Section: Grnn Based On Gwomentioning
confidence: 99%
“…The GWO algorithm is a swarm intelligence algorithm proposed by [30] with good self-organizing learning ability, simple parameters, easy implementation, and good global searchability [31], [32]. And by comparing with other four famous meta-heuristic algorithms (PSO algorithm, gravity search algorithm, differential evolution algorithm and fast evolutionary programing algorithm) on 29 test functions, the simulation test proves its superiority.…”
Section: Grnn Based On Gwomentioning
confidence: 99%
“…LS-SVM is based on concepts of machinery learning. For that we establish the model on two steps:  training  test In the first step, we consider a given training set [15][16][17][18][19][20] as follows:…”
Section: Least Squares Support Vector Machinesmentioning
confidence: 99%
“…However, the more used kernel function is RBF, a simple Gaussian function. It is defined by (16).  sv 2 is the squared variance of the Gaussian function.…”
Section: Least Squares Support Vector Machinesmentioning
confidence: 99%
“…As a typical cluster intelligent optimization algorithm, GWO [30] simulates the predation behavior of the grey wolf population, and compares the wolf group tracking process with the prey as the optimization process, so as to achieve the optimal solution. Applying GWO to the cluster head optimization problem of WSNs, the position of the wolf group represents the position of the sensor.…”
Section: Adaptive Grey Wolf Optimization Algorithm (Ad-gwo) Modelmentioning
confidence: 99%