Abstract

The study presents a new BP neural network algorithm to evaluate food network marketing performance. First, rough set is used to determine the attribute dimension of decision table and simplify the calculation structure of BP neural network algorithm; Second, genetic algorithm is modified to search the weights in BP algorithm calculation globally and optimize BP algorithm locally to avoid the network falling into the local extremes; Third, a new indicator system for evaluating food networking marketing performance is established focusing on enterprise value; Finally the improved algorithm is applied to evaluate food networking marketing performance and the experimental results show that the improved algorithm has great superiorities, such as simple algorithm process, fast convergence speed, get out local minimum easily and high evaluation accuracy.

Keywords:

Compost, Lentinus edodes residues (XG), Pleurotus ostreatus residues (PG), Scanning Electron Microscope (SEM),

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Competing interests

The authors have no competing interests.

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The authors have no competing interests.