Soft-Sensing Development Using Adaptive PSO Optimization Based Multi-Kernel ELM with Error Feedback

Xu, Yuan; Du, Qiang; Zhang, Mingqing; Zhu, Qunxiong; He, Yan‐Lin

doi:10.1109/ddcls.2018.8516051

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Theory Svm Basic Ideas and Parameters1

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2019

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“…The selection of penalty factor C and kernel parameter σ is very important for SVM classifier. Therefore, in order to achieve good SVM classification performance, it is necessary to select the appropriate C and σ [8].…”

Section: Theory Svm Basic Ideas and Parametersmentioning

confidence: 99%

Simulation of Indoor 60 GHz Millimeter Wave Propagation Characteristics Based on PSO Optimized SVM

Zhu¹,

Liu²,

Li³

2019

dtcse

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SVM (Support Vector Machine) is a modern Data-Mining method. It is good at processing large amounts of data and has high accuracy. In this paper, an indoor 60 GHz millimeter wave channel model based on PSO (Particle Swarm Optimization) optimized SVM is established. The prediction results of GA (Genetic Algorithm) and Grid-Search optimized SVM are compared. The simulation results show the PSO optimized SVM model has high prediction accuracy.

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Section: Theory Svm Basic Ideas and Parametersmentioning

confidence: 99%