Nonlinear Modeling of a Switched Reluctance Motor using LSSVM - ABC

doi:10.12700/aph.11.06.2014.06.9

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…The regression efficiency of the proposed LSSVM-ABC model [88] was verified against other models such as LSSVM, LSSVM-GA, LSSVM-PSO, and LSSVM-DE, as presented in Table 2. The LSSVM-ABC model offered the best modeling results, with a mean absolute error (MAE) of 0.066%.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 96%

“…Although the provided LSSVM algorithms in the earlier mentioned papers [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90] were efficient and precise, a potential downside is that the solution might deviate from the inlier samples if outliers existed. The inlier samples denote the TABLE 2.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…However, the unreasonable prediction accuracy and the relatively long computational time limit its use [79]. Therefore, several optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Differential Evolution (DE) have been proposed for hyperparameter optimization [80], [81], [82], [83], [84], [85], [86], [87], [88], [89], [90], [91].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…In [88], RBF was used as the kernel function. The traditional CV method was applied to tune the kernel and regularization parameters.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

“…The authors in [88] have employed LSSVM with Artificial Bee Colony (ABC) to model flux linkage characteristics of a 0.5-kW 4000-rpm 8/6 SRM. ABC is a meta-heuristic optimization algorithm that mimics the foraging behaviour of a bee swarm [89].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

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Review of Machine Learning Applications to the Modeling and Design Optimization of Switched Reluctance Motors

et al. 2022

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This work presents a comprehensive review of the developments in using Machine Learning (ML)-based algorithms for the modeling and design optimization of switched reluctance motors (SRMs). We reviewed Machine Learning-based numerical and analytical approaches used in modeling SRMs. We showed the difference between the supervised, unsupervised and reinforcement learning algorithms. More focus is placed on supervised learning algorithms as they are the most used algorithms in this area. The supervised learning algorithms studied in this work include the feedforward neural networks, recurrent neural networks, support vector machines, extreme learning machines, and Bayesian networks. This work also discusses several essential aspects of the considered machine learning algorithms, such as core concept, structure, and computational time. It also surveys sample data acquisition methods and data size. Finally, comparisons between the different considered ML-based algorithms are conducted in terms of electric motor type, dataset inputs and outputs, and algorithm's structure and accuracy to provide a summary overview of the ML-based algorithms for SRMs modeling and design.INDEX TERMS Electric machine design, electric machine modeling, machine learning (ML), switched reluctance motor (SRM).

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Section: Support Vector Machine (Svm)mentioning

confidence: 96%