Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization
文献类型：期刊论文
| 作者 | Liu, Ao1,2; Li, Peng3; Sun, Weiliang4; Deng, Xudong1,2; Li, Weigang5; Zhao, Yuntao5; Liu, Bo3 |
| 刊名 | NEURAL COMPUTING & APPLICATIONS
 |
| 出版日期 | 2020-05-01 |
| 卷号 | 32期号:10页码:5583-5598 |
| 关键词 | Neural networks Water wave optimization Meta-Lamarckian learning Prediction of mechanical properties |
| ISSN号 | 0941-0643 |
| DOI | 10.1007/s00521-019-04149-1 |
| 英文摘要 | 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 algorithm (WWOMA), we employ WWO to perform global search by both individual improvement and population co-evolution and then employ several local search components to enhance its local refinement ability. Moreover, an effective Meta-Lamarckian learning strategy is utilized to choose a proper local search component to concentrate computational efforts on more promising solutions. We carry out simulation experiments on six well-known neural network designing benchmark problems, both the simulation results and statistical comparisons demonstrate the feasibility, effectiveness and efficiency of applying WWOMA to design neural networks. Furthermore, we apply WWOMA to design neural networks and use well-trained neural networks to predict tensile strength of micro-alloyed steels. Evaluation on a practical industrial case with 2489 sample data shows that, in comparison with other algorithms, WWOMA-based neural networks can obtain notable and robust prediction accuracy, which further demonstrates that WWOMA is a promising and efficient algorithm for designing neural networks. It is worth mentioning that, to the best of our knowledge, this is the first report about applying water wave optimization to train neural networks. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000529745200024 |
| 出版者 | SPRINGER LONDON LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51399]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Liu, Bo |
| 作者单位 | 1.Wuhan Univ Sci & Technol, Sch Management, Wuhan 430065, Peoples R China 2.Wuhan Univ Sci & Technol, Ctr Serv Sci & Engn, Wuhan 430065, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 4.Tianjin Univ, Sch Chem Engn & Technol, Tianjin 300350, Peoples R China 5.Wuhan Univ Sci & Technol, Sch Informat Sci & Engn, Wuhan 430081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Ao,Li, Peng,Sun, Weiliang,et al. Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization[J]. NEURAL COMPUTING & APPLICATIONS,2020,32(10):5583-5598. |
| APA | Liu, Ao.,Li, Peng.,Sun, Weiliang.,Deng, Xudong.,Li, Weigang.,...&Liu, Bo.(2020).Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization.NEURAL COMPUTING & APPLICATIONS,32(10),5583-5598. |
| MLA | Liu, Ao,et al."Prediction of mechanical properties of micro-alloyed steels via neural networks learned by water wave optimization".NEURAL COMPUTING & APPLICATIONS 32.10(2020):5583-5598. |
入库方式： OAI收割
来源：数学与系统科学研究院
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