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

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Provided by: International Journal of Computer Science Issues
Topic: Data Management
Format: PDF
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).
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