Ash haze predicting system and method based on BP neural network

A BP neural network and haze technology, applied in neural learning methods, biological neural network models, predictions, etc., to achieve precise governance solutions, reduce prediction errors, and improve accuracy

Active Publication Date: 2016-11-02
CHINA THREE GORGES UNIV
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Problems solved by technology

Moreover, the natural environment and social environment where the haze is located have a self-learning nature, which is difficult for traditional forecasting methods to take into account
In China, with the application of data proces

Method used

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  • Ash haze predicting system and method based on BP neural network
  • Ash haze predicting system and method based on BP neural network
  • Ash haze predicting system and method based on BP neural network

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Embodiment Construction

[0035] Such as figure 1 As shown, a system of haze prediction based on BP neural network includes the following modules: data collection module 100, database 101, data sorting module 102, haze prediction server 103, data preprocessing module 104, neuron learning module 105 , BP neural network prediction module 106, WEB server 107 and mobile terminal 108.

[0036] The system describes the haze prediction method as a multi-input multi-output neural network prediction system, which predicts possible haze development trends through the self-learning mechanism of neurons; uses the BP neural network model to mine the gap between input and output data. The existing internal relationship continuously fits the correlation between the input data, reduces the prediction error through continuous feedback and learning mechanism, and provides a reference for haze prevention and control.

[0037] Described data collection module 100 comprises various sensors and monitoring equipment, is ins...

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Abstract

The invention provides an ash haze predicting system and method based on a BP neural network. The system excavates a relation among collected air quality data by means of the BP neural network, predicts air quality and ash haze weather development tendency, and provides early warning for the ash haze weather, and is characterized by comprising a data acquisition module, a database, a data arranging module, an ash haze prediction server, a data preprocessing module, a neuron learning module, a BP neural network prediction module, a WEB server and a mobile terminal. The method comprises steps of: describing the ash haze predicting system as a MIMO self-learning prediction system, inputting acquired ash haze and air quality data, predicting possible development tendency of ash haze weather by means of the self-learning and adaptive capability of the neural network, and reducing a prediction error. The ash haze predicting system and method may predict the ash haze development by using existing ash haze observations, deeply excavates a complex relation among input data, and obtain an accurate prediction result.

Description

technical field [0001] The invention relates to a haze prediction method and technology, in particular to a haze prediction system and method based on a BP neural network. Background technique [0002] At present, the haze phenomenon in China has attracted more and more attention, and researches on haze prediction analysis and governance strategies have emerged in an endless stream. implementation etc. The current haze prediction methods and systems mainly focus on the monitoring of ecological environment and air quality indicators for modeling, and use the monitored indicators such as PM2.5 and PM1.0 to try to accurately describe the mathematical model and evolution mechanism of haze. The latest technology also includes the deployment of sensor networks in haze-prone areas and satellite image analysis technologies, which provide a large amount of accurate data for haze monitoring and early warning, and then use this as a basis to analyze and predict the development trend o...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/08
CPCG06N3/084G06Q10/04
Inventor 蔡政英张余杨丽俊仵梦阳
Owner CHINA THREE GORGES UNIV
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