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Prediction of Soil Moisture Content Based on Improved BP Neural Network

Published: 18 August 2021 Publication History

Abstract

Aiming at the problem of slow convergence speed and easy to fall into local optimum of traditional BP neural network, an improved BP neural network algorithm is proposed in this paper. The algorithm trains BP neural network by small batch gradient descent method, and introduces adaptive learning factor to improve BP neural network. The learning rate of the algorithm can be adjusted with time to speed up the learning process of the multilayer feed-forward neural network. The verification shows that it effectively slows down the vibration phenomenon in the learning process, speeds up the learning process of BP algorithm, and has good robustness, and can complete the learning task with high precision.

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References

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ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems
May 2021
2053 pages
ISBN:9781450390200
DOI:10.1145/3469213
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 18 August 2021

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Author Tags

  1. BP neural network
  2. Small batch gradient descent method
  3. Soil moisture content

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