Abstract

An improved BP neural network algorithm for food demand prediction based on supply chain management is presented to realize market and sale management target effectively for food enterprises. First, the working principle of BP neural network algorithm is analyzed to explore the root reasons of its low convergence speed; Second, the paper integrates genetic algorithm with BP algorithm to present a new algorithm, then improves it through encoding chromosome, formatting fitness function, designing selection operator, redesigning crossover operator, designing mutation operator, integrating BP algorithm and optimal individual, improving calculation process step by step; Finally, a supply chain of a food enterprise is taken for experimental sample to illustrate the calculation performance of the improved algorithm and the simulation results indicate that the improved algorithm not only can solve the problem of low convergence speed, but also can improve the demand prediction accuracy and can be used for predicting supply chain demand for food enterprises practically.

Keywords:

BP neural network algorithm, food demand prediction, genetic algorithm, supply chain management,

References