LS-SVM Based Modeling And Forecast Of Operator Functional State In A Human-Machine System

The core problem of estimating Operator Functional State (OFS) in human-machine systems is construction of an appropriate mathematical model.This paper adopts Least Squares Support Vector Machine (LS-SVM) for OFS modeling based on a series of electrophysiological signals and operator performance data. model parameters are optimized by grid-search and 10-fold cross validation,and we get spare and robust model use modified LS-SVM algorithm proposed by Suykens.The simulation results show that LS-SVM has better generalization performance than GA-Mamdani,and it is efficient and feasible for OFS estimation use LS-SVM. The final model based on the results is used to adjust control strategies, achieving intelligent human-computer interaction.