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Design And Implementation Of Intelligent Calibration For Air Quality Monitoring Based On ANN

Posted on:2021-02-23Degree:MasterType:Thesis
Country:ChinaCandidate:X ZhangFull Text:PDF
GTID:2381330620963013Subject:Computer software and theory
Abstract/Summary:PDF Full Text Request
With the continuous rapid and healthy development of the current socialist market economy with Chinese characteristics,the industrialization process of the entire country,and the gradual increase in the level of material required by people 's lives,various environmental pollution problems in the air have become more serious and gradually become people 's lives The focus of attention.In addition to the various exhaust gases emitted by factories,the popularity of private cars has also led to the current worrying air environmental conditions.Relevant national departments have also begun to increase the governance of the air environment,and proposed related policies for grid monitoring of environmental quality.Under this background,many miniature monitoring instruments have emerged in the market,but due to insufficient accuracy of the internal sensors,there is a problem of data deviation.In order to solve this problem,this paper uses a large amount of standard text data and neural network technology to achieve an ANN-based intelligent calibration system for air quality monitoring.This project is based on artificial neural network technology,combining long short-term memory network(LSTM)models with collaborative training methods while using national standard data to complete the calibration model training.The calibration model can realize the error calibration of the monitoring data,and final y complete the design of the system.Firstly,the data collection function is designed to obtain the data for normalization processing.Then,the collaborative training method is used to perform cross-iterative training on unlabeled data and labeled data.After the labeling process of unlabeled data is completed,the Cotraining-The LSTM air quality calibration algorithm trains the data.The optimal learning rules are obtained through multiple iterations of training,which is used as the basis for data calibration to reduce the deviation of the data and achieve the functional task of accurate display of data monitoring.Through the performance test and system evaluation of ANN's air quality monitoring intel igent calibration system,the system can meet the requirements of the use level.
Keywords/Search Tags:Neural networks, Long and Short-term memory networks, Semi-supervised learning, Calibration
PDF Full Text Request
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