• Title/Summary/Keyword: MSMO

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Design of SVM-Based Gas Classifier with Self-Learning Capability (자가학습 가능한 SVM 기반 가스 분류기의 설계)

  • Jeong, Woojae;Jung, Yunho
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1400-1407
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    • 2019
  • In this paper, we propose a support vector machine (SVM) based gas classifier that can support real-time self-learning. The modified sequential minimal optimization (MSMO) algorithm is employed to train the proposed SVM. By using a shared structure for learning and classification, the proposed SVM reduced the hardware area by 35% compared to the existing architecture. Our system was implemented with 3,337 CLB (configurable logic block) LUTs (look-up table) with Xilinx Zynq UltraScale+ FPGA (field programmable gate array) and verified that it can operate at the clock frequency of 108MHz.