Least squares-support vector machine prediction method based on adaptive particle swarm

A technology of support vector machine and least squares, which is applied to computer parts, instruments, characters and pattern recognition, etc., can solve the problems of particle swarm missing the optimal solution, slowing down the convergence speed of the algorithm, and non-convergence of the algorithm, and achieve classification accuracy Good, fast convergence, memory-saving effect

Inactive Publication Date: 2017-03-15
CHINA UNIV OF MINING & TECH
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Problems solved by technology

[0018] It can be seen from the model of the particle swarm optimization algorithm PSO that if the acceleration factor c 1 ,c 2 Or if the value of the inertial weight factor ω is large, the particle swa

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  • Least squares-support vector machine prediction method based on adaptive particle swarm
  • Least squares-support vector machine prediction method based on adaptive particle swarm
  • Least squares-support vector machine prediction method based on adaptive particle swarm

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Embodiment Construction

[0051] Effect of the present invention can be further illustrated by following coal spontaneous combustion prediction:

[0052] Spontaneous combustion of coal is a very complex physical and chemical process. As the coal body oxidizes and heats up, it will release corresponding indicator gases, such as CO, CO 2 、CH 4 、C 2 h 6 、C 2 h 4 、C 2 h 2 , N 2 etc. Therefore, there is a very complex nonlinear relationship between the coal spontaneous combustion degree and gas products. When the coal quality is constant, the type, quantity, and temperature of the products have certain rules. Find out the corresponding relationship between these index gases and coal temperature, and monitor other indicators such as coal sample reaction gas products, temperature, and oxygen consumption. The signs of coal spontaneous combustion can be found, and the development trend of spontaneous combustion can be predicted.

[0053] 1. Experimental setup

[0054] In 2013, a coal sample was collec...

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Abstract

The invention relates to a least squares-support vector machine (LS-SVM) prediction method based on an adaptive particle swarm. According to the method, the inertia weight is adjusted according to the degree of convergence of groups and the adaptive values of individuals. Training is speeded up. A matrix equation appearing in an LS-SVM is iteratively calculated using the algorithm, and matrix inversion is avoided. Memory is saved. The optimal solution is obtained. By using the method, the training sample can be simplified effectively, and the training speed is improved. Moreover, the method has the advantages of high classification accuracy, fast convergence and good generalization ability. The problems in prediction like high feature dimension, redundancy between samples and a limited number of samples are solved.

Description

technical field [0001] The invention relates to a least square support vector machine prediction method based on adaptive particle swarm. Background technique [0002] Let the training set S={(x i ,y i )|i=1,2,…,m}, where x i ∈R n and y i ∈R is the input data and output data respectively, and the least squares support vector machine LS-SVM uses the structural risk minimization-SRM criterion to construct the minimum objective function J(ω,e) and its constraints are as follows: [0003] [0004] s.t.y i =w T Φ(x i )+b+e i [0005] Among them, w is the weight vector, γ is a constant, b is a constant bias, e i for the deviation. [0006] In order to solve the optimization problem of the above formula, it is transformed into solving the following linear equations: [0007] [0008] where Q=y i the y j Φ(x i ) T Φ(x i )=y i the y j K(x i ,x j ), K(x i ,x j ) is a kernel function satisfying the Mercer condition, I is the identity matrix, L=[1,1,…,1]∈R ...

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Application Information

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IPC IPC(8): G06N3/00G06K9/62
CPCG06N3/006G06F18/2411
Inventor 李海港张倩王德明曾磊程坤
Owner CHINA UNIV OF MINING & TECH
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