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Article Navigation >  Journal of University of Electronic Science and Technology of China  > 2008  >  37(6): 803-806

WAN Qun, WANG Wei, HUANG Ji-yan, SONG Yu-mei. Method for NLOS Mitigation Based on Fuzzy Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 803-806.
Citation: WAN Qun, WANG Wei, HUANG Ji-yan, SONG Yu-mei. Method for NLOS Mitigation Based on Fuzzy Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 803-806.
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Method for NLOS Mitigation Based on Fuzzy Support Vector Machines

  • 1.

    School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 610054

  • Received Date: 2007-07-02
  • Rev Recd Date: 2008-01-15
  • Publish Date: 2008-12-15
  • In order to overcome the overfitting problem caused by noises and outliers in support vector machine, a method for non-line-of-sight (NLOS) mitigation based on fuzzy least square support vector machines (LS-SVM) is proposed. Using the fuzzy membership model based on support vector data description (SVDD), the membership values to each input sample is computed according to its distance to the center of the hypersphere with minimal volume containing all objects. Simulation results show that the proposed method is robust in NLOS environments and actually increases the accuracy of LS-SVM.
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    沈阳化工大学材料科学与工程学院 沈阳 110142

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Method for NLOS Mitigation Based on Fuzzy Support Vector Machines

  • 1. School of Electronic Engineering,University of Electronic Science and Technology of China Chengdu 610054
  • Received Date: 2007-07-02
  • Rev Recd Date: 2008-01-15
  • Publish Date: 2008-12-15

Abstract: In order to overcome the overfitting problem caused by noises and outliers in support vector machine, a method for non-line-of-sight (NLOS) mitigation based on fuzzy least square support vector machines (LS-SVM) is proposed. Using the fuzzy membership model based on support vector data description (SVDD), the membership values to each input sample is computed according to its distance to the center of the hypersphere with minimal volume containing all objects. Simulation results show that the proposed method is robust in NLOS environments and actually increases the accuracy of LS-SVM.

WAN Qun, WANG Wei, HUANG Ji-yan, SONG Yu-mei. Method for NLOS Mitigation Based on Fuzzy Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 803-806.
Citation: WAN Qun, WANG Wei, HUANG Ji-yan, SONG Yu-mei. Method for NLOS Mitigation Based on Fuzzy Support Vector Machines[J]. Journal of University of Electronic Science and Technology of China, 2008, 37(6): 803-806.
shu

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