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Fast Modeling Methods for Complex System with Separable Features
Chen C(陈辰); Luo ZT(罗长童); Jiang ZL(姜宗林)
Source Publication2017 10TH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL. 1
2017
Conference Name10th International Symposium on Computational Intelligence and Design (ISCID)
Conference DateDEC 09-10, 2017
Conference PlaceHangzhou, PEOPLES R CHINA
Abstract

Data-driven modeling plays an increasingly important role in different areas of engineering. For most of existing methods, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. Fortunately, in many applications, the target models are separable in some sense. In this paper, we analyze different types of separability and establish a generalized separable model (GSM). In order to get the structure of the GSM, a multi-level block search method is proposed, in which the target model is decomposed into a number of blocks, further into minimal blocks and factors. Compare to the conventional GP, the new method can make large reductions to the search space. The minimal blocks and factors are optimized and assembled with a global optimization search engine, low dimensional simplex evolution (LDSE). An extensive study between the proposed method and a state-of-the-art data-driven fitting tool, Eureqa, has been presented with several man-made problems. Test results indicate that the proposed method is more effective and efficient under all the investigated cases.

KeywordData-driven Modeling Genetic Programming Generalized Separable Model Multi-level Block Search
WOS IDWOS:000427991100045
Funding OrganizationThis work was supported by the National Natural Science Foundation of China (Grant No. 11532014).
ISBN978-1-5386-3675-6
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Indexed ByEI
Language英语
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Document Type会议论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/75552
Collection高温气体动力学国家重点实验室
Recommended Citation
GB/T 7714
Chen C,Luo ZT,Jiang ZL. Fast Modeling Methods for Complex System with Separable Features[C],2017.
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