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