Fracture performance prediction of polyvinyl alcohol fiber-reinforced cementitious composites containing nano-SiO2 using least-squares support vector machine optimized with quantum-behaved particle swarm optimization algorithm

2021 ◽  
pp. 103074
Author(s):  
Tingyu Liu ◽  
Peng Zhang ◽  
Guo Cui ◽  
Xiaodong Yue
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Polyvinyl Alcohol ◽  
Performance Prediction ◽  
Particle Swarm Optimization Algorithm ◽  
Support Vector ◽  
Swarm Optimization ◽  
Polyvinyl Alcohol Fiber ◽  
Fracture Performance ◽  
Fiber Reinforced Cementitious Composites

scholarly journals Parameter Selection of a Support Vector Machine, Based on a Chaotic Particle Swarm Optimization Algorithm

2015 ◽  
Vol 15 (3) ◽  
pp. 140-149 ◽  
Author(s):  
Huang Dong ◽  
Gao Jian
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Parameter Selection ◽  
Support Vector ◽  
Optimal Parameters ◽  
Swarm Optimization ◽  
Chaotic Particle Swarm Optimization ◽  
Selection Of

Abstract This paper proposes a SVM (Support Vector Machine) parameter selection based on CPSO (Chaotic Particle Swarm Optimization), in order to determine the optimal parameters of the support vector machine quickly and efficiently. SVMs are new methods being developed, based on statistical learning theory. Training a SVM can be formulated as a quadratic programming problem. The parameter selection of SVMs must be done before solving the QP (Quadratic Programming) problem. The PSO (Particle Swarm Optimization) algorithm is applied in the course of SVM parameter selection. Due to the sensitivity and frequency of the initial value of the chaotic motion, the PSO algorithm is also applied to improve the particle swarm optimization, so as to improve the global search ability of the particles. The simulation results show that the improved CPSO can find more easily the global optimum and reduce the number of iterations, which also makes the search for a group of optimal parameters of SVM quicker and more efficient.

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