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Particle swarm optimization-based least square support vector machine combined predicting method

A support vector machine and particle swarm optimization technology, applied in the field of information processing, can solve the problems of sensitive model setting, unsatisfactory prediction effect of a single model, and insufficient information sources.

Inactive Publication Date: 2013-04-03
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0003] However, there are some defects in using a single forecasting model for forecasting, such as the lack of extensive information sources, sensitivity to model setting forms, etc., which makes the forecasting effect of a single model often unsatisfactory

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  • Particle swarm optimization-based least square support vector machine combined predicting method
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  • Particle swarm optimization-based least square support vector machine combined predicting method

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Embodiment Construction

[0038] A kind of least squares support vector machine combined prediction method based on particle swarm optimization described in the present invention, such as figure 1 Example shown. Verification is carried out under 70%, 80%, 90% to test sample and training sample ratio, to verify the validity of the present invention. Using the BP neural network model, AR model, and GM(1,1) model to obtain the sample values ​​and calculate the sample error, it can be seen from Table 1 that the prediction results of the combined prediction model established by the LSSVM method under different sample ratios are consistent. The error is obviously smaller than the prediction error of BP neural network, AR model and GM (1,1) model, which verifies the validity and superiority of the present invention. Especially when the sample ratio is 90%, the prediction effect is the best, and the prediction effect figure 2 , image 3 , Figure 4 , Figure 5 shown; where figure 2 , image 3 , Figu...

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Abstract

The invention provides a particle swarm optimization-based least square support vector machine combined predicting method. The particle swarm optimization-based least square support vector machine combined predicting method comprises the following steps: according to data characteristics to be predicted, selecting proper single predicting models; properly combining different predicting methods; by making full use of useful information contained in the single predicting models, establishing an LSSVM (least square support vector machine) regression model; and through a PSO (particle swarm optimization), optimizing two core parameters which affect the precision of the LSSVM regression model and include a kernel function parameter g and an LSSVM regularization parameter C so as to obtain the optimal LSSVM regression model. By the method, the aims of improving the predicting precision and reducing predicting risks can be achieved; the convergence rate of the algorithm is greatly improved; and actual engineering needs can be met better.

Description

technical field [0001] The invention belongs to the technical field of information processing, in particular to a least square support vector machine combination prediction method based on particle swarm optimization. Background technique [0002] Forecasting science emerges with the continuous development of social economy. Forecasting experts use certain methods, models and procedures to analyze and study the relationship between forecasting objects and related factors through historical statistical data and current actual information, so as to deeply reveal the change law of forecasting objects and speculate predict the future development direction and results of the object. On this basis, forecasting activities belong to the activities of exploring the future, which fully embodies human beings' exploration and control of the future world. The existing forecasting models mainly include time series forecasting model, gray forecasting model, BP neural network forecasting ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 李爱陈果王洪伟程小勇郝腾飞于明月
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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