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

DOI

10.2991/978-94-6463-344-3_37How to use a DOI?

Keywords

Support Vector Machine; Time series; Nonlinear dynamics; Cusp mutation

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.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jing Wang
PY  - 2024
DA  - 2024/01/10
TI  - Research on a Nonlinear Dynamic Model Support Vector Machine Based for Rock Mass Evolution
BT  - Proceedings of the 2023 2nd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2023)
PB  - Atlantis Press
SP  - 328
EP  - 334
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-344-3_37
DO  - 10.2991/978-94-6463-344-3_37
ID  - Wang2024
ER  -