PM2.5 concentration prediction method based on stack self-encoding and support vector regression

A technology of support vector regression and stacked self-encoding, which is applied in prediction, instrumentation, biological neural network model, etc., can solve problems such as inability to obtain higher accuracy, cannot exceed one or two hidden layers, etc., and achieve excellent prediction performance. Effect

Inactive Publication Date: 2019-01-04
HOHAI UNIV
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Problems solved by technology

[0006] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a PM2.5 concentration prediction method that combines stacked self-encoding and support vector re

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  • PM2.5 concentration prediction method based on stack self-encoding and support vector regression
  • PM2.5 concentration prediction method based on stack self-encoding and support vector regression
  • PM2.5 concentration prediction method based on stack self-encoding and support vector regression

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[0031] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0032] In the field of artificial intelligence, the ANN optimization algorithm is the most widely used algorithm with excellent performance, but it can never go beyond one or two hidden layers in non-convex optimization problems, resulting in the inability to learn deep networks to achieve higher accuracy. In order to overcome this problem, a SAE with multiple sparse autoencoders as hidden layers is introduced to extract the features of the dataset, and then the SVR model is used to improve the prediction performance. Based on this, the present invention proposes a PM2.5 concentration prediction method combining stacked autoencoder and support vector regression.

[0033] Such as figure 1 As shown, the PM2.5 concentration prediction method that combines stacked autoencoder and support vector regression includes the following steps:

[0034...

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Abstract

The invention discloses a PM2.5 concentration prediction method which combines stack-type self-coding and support vector regression. Steps are as follows: selecting influential factors with strong correlation as independent variables of the PM2.5 concentration prediction model, continuously collecting the selected independent variables and corresponding PM2.5 concentration data for N hours, and constructing a training set; a deep learning network model of stack self-encoder with K hidden layers being constructed; the training set being input into the model for training and the weights of eachvariable being obtained; the SVR model being trained by using the eigenvalues of the learning network model as input vectors, and the output of the SVR model being obtained; the test set being obtained, the test sample being predicted by the predictive model, and the precision of the predictive result being calculated. The invention has the advantages of SAE extracting data set features and SVR excellent prediction ability, and the prediction performance and precision are better than widely used ANN algorithm and SVR algorithm.

Description

technical field [0001] The invention belongs to the field of air quality prediction, and in particular relates to a PM2.5 concentration prediction method. Background technique [0002] In recent years, with the serious decline in air quality around the world, in order to better warn the occurrence of haze weather, some scholars and business organizations at home and abroad have also begun to pay attention to the effective prediction of fine particles in the air. [0003] At present, the commonly used air quality forecasting techniques are mainly divided into two categories: statistical forecasting and numerical forecasting. Among them, the statistical forecast is generally based on the correlation model between historical data and meteorological conditions, and then uses the meteorological conditions in the future and the correlation model established to predict the future air quality. This method has low requirements for input data, but the prediction results are generally...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/26G06N3/04
CPCG06Q10/04G06Q50/26G06N3/045
Inventor 刘凡朱一行李峥嵘毛莺池许峰
Owner HOHAI UNIV
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