Abstract

Performance evaluation of integrate with agriculture food base and supermarket is a research hotspot and difficulty in the theory and practice research of agriculture super-docking mode. The study presents an evaluation indicator system and a fuzzy neural network evaluation algorithm for evaluating performance of integrates with agriculture food base and supermarket. Firstly, a performance evaluation indicator system is designed through analyzing the similarities of general performance evaluation and the specialties of the evaluation of integrates with agriculture food base and supermarket; Secondly, the study integrates the advantages of fuzzy evaluation methods and BP neural network evaluation methods, designs a new algorithm structure, selects different learning methods and analyzes the algorithm performance, then presents a new fuzzy neural network evaluation algorithm; Finally, three integrates are taken for experimental examples and the results illustrate that the improved algorithm can be used for evaluating the performance of integrates with agriculture food base and supermarket feasibly and effectively and can provide reference for evaluating other complex systems.

Keywords:

BP neural network, fuzzy evaluation method, integrate with agriculture food base and supermarket, performance evaluation,

References

1. Fei, D.F. and D. Wiesel, 2013. An improved DEA algorithm based on factor analysis. J. Convergence Inform. Technol., 5(4): 103-108.
   
2. John, T., T. Hanazawa and Manuela, 2013. Analyzing the complex system evaluation based on fuzzy information. J. Comput. Appl., 10(10): 1301-1310.
   
3. Mrel, A. and J. Sengio, 2012. Fuzzy quality attributes for evaluating integrates with agriculture food base and supermarket. J. Syst. Eng., 10(12): 931-944.
   
4. Sarnowski, T., 2012. Usage and effectiveness of online marketing tools among business-to-consumer (B2C) firms in Singapore. Int. J. Data Collect. Process., 11(4): 421-430.
   
5. Sarochelle, G. and T. Laxe, 2012. Categorizing web features and functions to evaluate commercial web sites. Ind. Manage. Data Syst., 7(11): 28-39.
   
6. Smolensky, Y. and J. Hanazawa, 2013. Fuzzy internet marketing system performance. J. Inform. Eng., 10(12): 931-936.
   
7. Vinton, R. and M. Janbiner, 2012. A framework and methodology for evaluating integrates with agriculture food base and supermarket. Internet Res., 8(2): 231-243.
   
8. Yann, S. and S. Jerouxn, 2012. Quantitative assessment of European municipal web sites: Development and use of an evaluation tool. Internet Res., 7(11): 28-39.
   
9. Yobin, Y. and H. Hinton, 2012. Optimal model of complicated system evaluation based on linear weighting. Ind. Eng. J., 18(9): 77-87.
   
10. Yohan, L., 2010. Construction of BPMN-based business process model base. Int. J. Intell. Inform. Process., 15(2): 32-48.
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Competing interests

The authors have no competing interests.

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The authors have no competing interests.