Blasting vibration predicting method based on particle swarm algorithm optimization support vector machine

Abstract

The present invention provides a blasting vibration predicting method based on a particle swarm algorithm optimization support vector machine. According to the method, firstly, blasting vibration influence factors are subjected to feature extraction. Secondly, the kernel function, the penalty factor, the slack variable and the kernel parameters of the support vector machine are subjected to combined optimization thorough the improved PSO algorithm, and then an optimal support vector regression machine and an optimal support vector classifier model are respectively obtained. In this way, the classified prediction of blasting vibration data is realized. Compared with the traditional blasting vibration velocity predicting method for support vector machines, optimized combined parameters are obtained, so that the performances of models can be better improved. The prediction accuracy of models is improved, and the prediction accuracy of the blasting vibration strength is greatly improved.

Description

technical field [0001] The invention belongs to the field of machine learning, and in particular relates to a blasting vibration prediction method based on particle swarm algorithm optimization support vector machine. Background technique [0002] Blasting vibration prediction is to study the relationship between blasting vibration influencing factors and blasting vibration intensity. The influencing factors include a series of factors such as explosive unit consumption, aperture parameters, geological conditions and blast center distance, while vibration intensity usually refers to the vibration generated by blasting. The strength of the wave can be described by the blasting vibration velocity, frequency and blasting vibration time, etc., especially the peak value of the blasting vibration velocity is more common. [0003] Prediction of blasting vibration is an effective method to reduce the risk factor of blasting, optimize the blasting scheme and evaluate the safety level...

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Problems solved by technology

[0005] Aiming at the deficiency that the support vector regression machine (SVR) can predict the blasting vibration velocity in the prior art, but the vibration intensity cannot be predicted, the present invention proposes a method combining support vector classification machine (SVC) and support vector regression machine (SVR) to To predict the safety factor of blasting vibration, on the basis of feature extraction by principal component analysis method, the improved particle swarm optimization algorithm (PSO) is used to combine and optimize the parameters in the prediction model to improve the prediction accuracy

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