2012Advanced materials researchOpen access

The Application on the Forecast of Plant Disease Based on an Improved BP Neural Network

Bao Shi Jin, Yu Zuo, Dan Xiao, Hai Ou Guan, Feng Tan

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Abstract

Aiming at the disadvantages of large computing, slow convergence and easily trapping into local minima of traditional BP network, a new method named batch momentum learning algorithm which combining the momentum with batch gradient descent algorithm has been used to be as the learning algorithm of connection weights and threshold of BP neural network, through using this method to forecast the prevalence of plant disease, the convergence speed of BP neural network has been enhanced.

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What this paper is about

Aiming at the disadvantages of large computing, slow convergence and easily trapping into local minima of traditional BP network, a new method named batch momentum learning algorithm which combining the momentum with batch gradient descent algorithm has been used to be as the learning algorithm of connection weights and threshold of BP neural network, through using this method to forecast the prevalence of plant disease, the convergence speed of BP neural network has been enhanced.

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Available abstract

Aiming at the disadvantages of large computing, slow convergence and easily trapping into local minima of traditional BP network, a new method named batch momentum learning algorithm which combining the momentum with batch gradient descent algorithm has been used to be as the learning algorithm of connection weights and threshold of BP neural network, through using this method to forecast the prevalence of plant disease, the convergence speed of BP neural network has been enhanced.

Key concepts: Maxima and minima, Artificial neural network, Convergence (economics), Gradient descent, Momentum (technical analysis), Backpropagation, Computer science, Connection (principal bundle)

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