Thursday 23rd of November 2017
 

Application of SVM Optimization Based on GA in Electronic Sphygmomanometer Data Fusion


Fengmei Gao and Tao Lin

If the proper kernel function parameter? is chosen, using of the multi-sensor data fusion method based on SVM, the influence of cross sensitive disturbance variables including the temperatureT and the power supply current I , can be significantly suppressed and the stability of the pressure sensor can be improved in the electronic sphygmomanometer. While kernel function parameter? is difficult to ascertain after repeated test. GA(Genetic Algorithm) with powerful global searching for optimal solutions is able to meet the requirement of optimization for kernel function parameter? of SVM(Support Vector Machine).

Keywords: SVM, GA, Kernel Function Parameter, Multi-sensor Data Fusion

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ABOUT THE AUTHORS

Fengmei Gao
School of Life Scienes and Technology, Xinxiang Medical University Xinxiang, Henan, 453003,China

Tao Lin
School of Automation, Chongqing University Chongqing, 400044, China


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