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Multi-parameter online measurement IoT system based on BP neural network algorithm


Aiming at the problems of long measurement period of online measurement parameters and untimely feedback of IoT technology based on wireless sensor network, this paper designs a multi-parameter online measurement method based on BP neural network algorithm. The collection, analysis, processing and display of parameters are completed through the sensing layer, the network transmission layer and the integrated application layer. The BP neural network algorithm is added to the integrated application layer to optimize the real-time acquisition parameters to shorten the parameter measurement time and accurate prediction. That is, based on the collection of environmental information, by training and learning the BP model with known historical data, it is possible to predict the environmental value at a later time. The known three-layer forward propagation (BP) neural network has the property of approximating the nonlinear curve, and it can achieve good results by predicting the temperature trend. The experimental results show that the system has better ability to monitor and predict the temperature trend. The algorithm simulation experiment shows that the online measurement system based on BP neural network algorithm proposed in this paper can speed up the data collection time, accurately predict the trend of environmental parameters and provide timely warning for potential safety hazards.

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Correspondence to Jingqing Liu.

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Zhang, W., Kumar, M. & Liu, J. Multi-parameter online measurement IoT system based on BP neural network algorithm. Neural Comput & Applic 31, 8147–8155 (2019).

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  • Internet of Things technology
  • Perception layer
  • Integrated application layer
  • BP neural network