Computer Science ›› 2018, Vol. 45 ›› Issue (12): 170-176.doi: 10.11896/j.issn.1002-137X.2018.12.027

• Artificial Intelligence • Previous Articles     Next Articles

Physical Quantity Regression Method Based on Optimized BP Neural Network

PAN Jun-hong1,2,3, WANG Yi-huai2,4, WU Wei1,2,3   

  1. (School of Mathematics and Computer,Wuyi University,Wuyishan,Fujian 354300,China)1
    (Department of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China)2
    (Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions,Wuyishan,Fujian 354300,China)3
    (Collaborative Innovation Center of Novel Software Technology and Industrialization,Suzhou,Jiangsu 215006,China)4
  • Received:2017-11-21 Online:2018-12-15 Published:2019-02-25

Abstract: In the development of practical application system of Internet of Things(IoT),traditional regression methods have the problems of non-uniform expression,poor non-linear correction ability and dynamic adaptability for physical quantity regression of A/D conversion.Based on the analysis of the physical quantity regression elements of A/D conversion,and according to the non-linear mapping ability in BP artificial neural network,this paper proposed a BP neural network optimized by cuckoo search algorithm,and utilized it to realize physical quantity regression method of A/D conversion with unified mathematical expression.Practice shows that this method has the characteristics of uniform mathematical formula,strong nonlinear correction ability and dynamic adaptability.This method is not only suitable for IoT system using communication methods to send A/D acquisition data to PC directly,but also suitable for the environment in which PC is used to learn.The neural network structure parameters are stored in the Flash of MCU,and the A/D value is directly converted to the actual physical quantity at the terminal IoT.

Key words: A/D conversion, BP neural network, Cuckoo search algorithm, Dynamical correction, Physical quantity regression

CLC Number: 

  • TP391
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