Hao Li1,2, Zhixia Wang1,2, Wei Wang1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.2, pp. 765-782, 2020, DOI:10.32604/cmes.2020.010061
Abstract Extracting nonlinear governing equations from noisy data is a central
challenge in the analysis of complicated nonlinear behaviors. Despite researchers
follow the sparse identification nonlinear dynamics algorithm (SINDy) rule to
restore nonlinear equations, there also exist obstacles. One is the excessive dependence on empirical parameters, which increases the difficulty of data pre-processing. Another one is the coexistence of multiple coefficient vectors, which causes
the optimal solution to be drowned in multiple solutions. The third one is the composition of basic function, which is exclusively applicable to specific equations. In
this article, a local sparse screening identification algorithm (LSSI) is proposed… More >
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