NDLI: Application of particle swarm optimization for tuning the SVR parameters

Content Provider	IEEE Xplore Digital Library
Author	Xin-qing Wang Juan Gao
Copyright Year	2015
Description	Author affiliation: China Univ. of Pet., Qingdao, China (Xin-qing Wang) \|\| Qingdao Harbour Vocational & Tech. Coll., Qingdao, China (Juan Gao)
Abstract	The prediction of finger pinch force via surface electromyography (sEMG) signals is important in bionic control area. The purpose of this paper was to study how to improve the prediction accuracy while using support vector regression (SVR) to predict the pinch force. Four healthy subjects performed constant-posture force-varying pinch operations. The sEMG signal was acquired using two electrodes while the force signal was recorded by a JR3 sensor. The time domain feature of sEMG and the force signal were then applied as the input of the SVR model. In order to improve the prediction accuracy, the parameters of SVR model were optimized by applying particle swarm optimization (PSO) algorithm. The relative mean square error (RMSE), correlation coefficients (CC), and mean average error (MAE) were calculated as the criteria. The results show that the predicted force is close to the real pinch force by SVR modeling technique. The RMSE results are below 8% and the CC results are above 96% with 4 subjects. Compared with the grid search (GS) method, the PSO-SVR achieves a tradeoff between the accuracy and the computational costs with different kinds of training data.
Sponsorship	IEEE Ind. Electron. Soc. (IES)
Starting Page	1173
Ending Page	1177
File Size	544290
Page Count	5
File Format	PDF
e-ISBN	9781467391078
DOI	10.1109/AIM.2015.7222697
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-07
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Force Support vector machines Muscles Optimization Electromyography Fingers Electrodes
Content Type	Text
Resource Type	Article

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