Research on a Nonlinear Dynamic Model Support Vector Machine Based for Rock Mass Evolution

Jing Wang ()
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Jing Wang: Xi’an International University, Department of Architectural Engineering, Faculty of Engineering

A chapter in Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023), 2023, pp 328-334 from Springer

Abstract: Abstract This article is based on time series and applies support vector machine to establish a nonlinear dynamic model of rock mass evolution. The longest predictable time is given based on the Lyapunov index, and a nonlinear dynamic model prediction model based on support vector machine is proposed through function fitting. The nonlinear dynamic model is combined with nonlinear catastrophe theory to timely reflect the evolution direction of rock mass and make predictions and judgments on its stability, Use mutation theory to analyze its stability. The results indicate that the model has ideal prediction performance and good generalization ability.

Keywords: Support Vector Machine; Time series; Nonlinear dynamics; Cusp mutation (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-344-3_37

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DOI: 10.2991/978-94-6463-344-3_37

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