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Particle swarm optimization algorithm and support vector machine applied research

Author: XuShuiHua
Tutor: MoYuanBin
School: Guangxi University for Nationalities
Course: Computational Mathematics
Keywords: Particle Swarm Optimization Complex method Mean function Mean particle swarm optimization Support Vector Machine Kernel function
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 148
Quote: 1
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Abstract


Intelligent optimization algorithm is a mathematical basis , through mathematical modeling and composition , with the help of modern computer tools mimic the natural , social laws designed to solve the problem of the algorithm. Particle swarm optimization algorithm and support vector machine is intelligent algorithm is an important two algorithms . Particle swarm optimization algorithm is a swarm intelligence optimization algorithm, first proposed by Kennedy and Eberhar raised . PSO algorithm is due to bird foraging behavior and be inspired by and designed for solving optimization problems. Algorithm, each particle represents a feasible solution of the problem , each corresponding to a feasible solution is determined by the objective function fitness value . Velocity of the particles determines the motion of the particle 's next position, velocity and other particles according to their flying experience dynamically adjusted , thereby completing the individual in the feasible solution space of the optimization process . SVM (Support Vector Machine, referred SVM) was in the 1990s , by Vapnik and his team in the years of study on the basis of statistical learning theory, put forward a new learning algorithm, which is mainly used to solve the pattern recognition and regression forecasting problem . Algorithm practical problems through nonlinear mapping transition to high-dimensional feature space is constructed in high-dimensional space linear discriminant function to achieve the original non-linear discriminant function space , making its algorithm complexity and sample dimension regardless cleverly solved the dimension of the problem . In this paper, the PSO algorithm and SVM have made a thorough research. ( 1 ) In the particle swarm algorithm based on the combination of complex method of local search capabilities , made ​​with local search capabilities PSO ; ( 2 ) on particle swarm optimization (PSO) overall maximum, local minimum value adjustment model based on particle swarm algorithm to solve the problem of the function for solving the mean ; ( 3 ) in the study support vector machine, combined with the characteristics of different kernel functions to construct mixed kernel function, and mixed kernel SVM model used in cardiac diagnosis ; ( 4 ) the article also studied the support vector regression , while taking advantage of the model on the Shanghai index for prediction. The paper also takes advantage of the PSO algorithm for support vector machine parameters are optimized , and achieved good results.

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CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
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