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A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks

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Published in:IEEE Access
Format: Online Article RSS Article
Published: 2026
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container_title IEEE Access
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discipline_display Engineering & Technology
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format Online Article
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publishDate 2026
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spellingShingle A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
sub_discipline_display Computer Science & IT
sub_discipline_facet Computer Science & IT
subject_display Computer Science & Information Science
Computer Science & IT
Engineering & Technology
Computer Science & Information Science
Computer Science & IT
Engineering & Technology
subject_facet Computer Science & Information Science
Computer Science & IT
Engineering & Technology
title A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_auth A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_full A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_fullStr A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_full_unstemmed A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_short A Computationally Efficient Multi-Objective Design Optimization of SRM Using K-Means Clustering and Artificial Neural Networks
title_sort a computationally efficient multi-objective design optimization of srm using k-means clustering and artificial neural networks
topic Computer Science & Information Science
Computer Science & IT
Engineering & Technology
url http://ieeexplore.ieee.org/document/11407487