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Improved particle swarm optimization LSSVM spatial location trajectory data prediction model in health care monitoring system

  • Guobin ChenEmail author
  • Zhang Li
Original Article

Abstract

With the real-time recording of the health care system, real-time monitoring of user trajectory data can effectively protect the health and health of the body. In order to accurately establish the spatial position trajectory data prediction model, a space trajectory data prediction model based on variable space chaotic particle swarm optimization (CPSO)-optimized least squares support vector machine (LSSVM) is proposed. Firstly, aiming at the problem that particle swarm optimization (PSO) is easy to fall into local optimum, the global optimization performance of PSO algorithm is improved by using variable space chaos search strategy and particle out-of-bounds mirror processing strategy. Numerical simulation experiments verify the superiority of improved particle swarm optimization algorithm. Based on the CPSO algorithm, the hyper parameters of the CPSO-optimized least squares support vector machine (CPSO-LSSVM) are proposed to improve the prediction accuracy of the model. Finally, using GeoLife and T-Drive dataset as the research object, the CPSO-LSSVM spatial position prediction model is established by using the trajectory data of the dataset. The simulation experiment verifies that the CPSO-LSSVM positional space prediction model has higher prediction accuracy and more strong generalization ability to accurately and effectively predict spatial location.

Keywords

Health care Real-time monitoring system Spatial position Particle swarm optimization Least squares support vector machine 

Notes

Funding information

This work was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (KJQN201802101, KJZD-K201802101).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Chongqing Key Laboratory of Spatial Information Data Mining and Big Data Integration for Ecological EnvironmentRongzhi College of Chongqing Technology and Business UniversityChongqingChina

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