Least square support vector machine (LSSVM) periodic weighting prediction method based on wavelet and chaos optimization

Abstract

The invention provides a least square support vector machine (LSSVM) periodic weighting prediction method based on wavelet and chaos optimization. The method utilizes the wavelet decomposition technology to enable selected sample set data to be decomposed into components of different frequencies. A component phase space is reconstructed on the basis of the chaos theory, the reconstructed components are trained by using LSSVM models, parameters of LSSVM prediction models are optimized through chaos particle swarm optimization, and finally wavelet reconstruction is performed on prediction components obtained by the LSSVM models to whole periodic weighting load prediction waveforms. Through wavelet decomposition of a periodic load, reconstruction of a load phase space on the basis of the chaos theory, construction of a least square support vector machine, and chaos particle swarm optimization of the parameters, the LSSVM periodic weighting prediction method based on the wavelet and the chaos optimization can predict periodic weighting load waves at some period of reconstructed waves when load time sequence has certain chaos characteristics and can be widely used for prediction of periodic weighting load to be born of a support.

Description

technical field [0001] The present invention relates to The problem of cyclic load prediction in underground mining engineering, especially involving Periodic pressure prediction method based on wavelet and chaos optimization LSSVM. Background technique [0002] The hydraulic support in the roadway is the main component that bears the pressure of the roof. Many factors should be considered when setting up the support, among which the form and change law of the load are the main consideration factors. How to understand the load form and changing law of the future hydraulic support based on the existing data has become a key issue. In order to realize the safe and efficient production of the working face, the cycle, step distance and strength must be mastered. Therefore, scientific methods must be used to correctly predict the periodic pressure waveform. The traditional prediction methods mainly include: empirical estimation method, Wilson estimation method, and old...

AI Technical Summary

Problems solved by technology

[0003] In view of the fact that there are few researches on period pressure forecasting and the accuracy is poor, a LSSVM method based on wavelet and chaos optimization is constructed for forecasting

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