An Improved BP Neural Network Algorithm and its Application in Competitive Advantage Evaluation of Logistics Enterprises

Abstract:

BP neural network is a hot research field for its powerful simulation calculation ability in various disciplines in recent years, but the algorithm has some shortages such as low convergence which limit the usage of the algorithm. The paper improves BP model with genetic algorithm and applies it to evaluate competitive advantages of logistics enterprises. First the paper designs an evaluation indicator system of competitive advantage of logistics enterprises through analyzing the characteristics of the evaluation indicator; Second, genetic algorithm is used to speed up the convergence of BP algorithm and based on this the paper advances a new competitive advantage evaluation model for logistics enterprises. Finally, the improved model is realized with the data from four Chinese logistics enterprises and the realization of the experimental results show that the model can improve algorithm efficiency and evaluation accuracy and can be used for evaluating the competitive advantages of logistics enterprises practically.

• Periodical: Advanced Materials Research (Volumes 850-851)
• Pages: 788-791
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.850-851.788
• Online since: December 2013
• Authors: Feng Lan Luo
• Keywords: BP Neural Network (BPNN), Competitive Advantage Evaluation, Evaluation Indicator System, Genetic Algorithm (GA)

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