Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization

Searching optimal parameters for neural networks can be formulated as a multi-modal optimization problem. This paper proposes a novel water wave optimization (WWO)-based memetic algorithm to identify the optimal weights for neural networks. In the proposed water wave optimization-based memetic algor...

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Veröffentlicht in:	Neural computing & applications Jg. 32; H. 10; S. 5583 - 5598
Hauptverfasser:	Liu, Ao, Li, Peng, Sun, Weiliang, Deng, Xudong, Li, Weigang, Zhao, Yuntao, Liu, Bo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.05.2020 Springer Nature B.V
Schlagworte:	Advances in Parallel and Distributed Computing for Neural Computing Algorithms Alloying Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Computer simulation Data Mining and Knowledge Discovery High strength low alloy steels Image Processing and Computer Vision Mechanical properties Microalloying Neural networks Optimization Probability and Statistics in Computer Science Searching Tensile strength Water waves Meta-Lamarckian learning Water wave optimization Neural networks Prediction of mechanical properties
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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