FPGA Implementation of Classification Based on SVM For Smart Grid Application
Keywords:
FPGA devices , support vector machine and smart grid.Abstract
In order to increase predictive accuracy, Support Vector Machines (SVMs), a common classification and regression prediction tool, employ supervised machine learning theory. The field programmable gate array (FPGA) implementation of a classification system using a support vector machine is the main emphasis of this study. The FPGA-based two-class SVM classifier can rapidly classify data due to the powerful parallel computation capability it offers. Depending on the classification's dimensions, the system operates in either a linear or non-linear fashion. The system would help in the classification system which is efficient at quickly classify data and is a promising method for enhancing communication security in the Smart Grid.
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