Synthetic Evaluation of the Food Processing Enterprise Alliance Ability Based on Hopfield Neural Network Improved by Schimidt Method

摘要

Food processing enterprise alliance as one innovation model of modern food processing enterprise strategic has become the important tools to improve the food processing enterprise competitive edge. Food processing enterprise alliance innovative ability has received great attention for the remarkable performance impetus. However, the complexity and integrity of food processing enterprise alliance innovative ability make no consensus in the conception and the evaluation. Based on the angle of process management, the study takes knowledge protection capacity (pre-alliance), cooperation regulation establishing capacity and relationship development and maintenance capacity (post-alliance) as main alliance ability and proposes an improved Discrete Hopfield Neural Network (S-DHNN) to evaluate alliance capacity. In view that the source of sample data is questionnaire statistical result, the study introduces noise with different intensity to simulate the questionnaire's subjectivity and randomness, whose result will be compared to other method such as traditional DHNN, Fuzzy synthetic evaluation model and Cluster analysis. The conclusion shows that the proposed S-DHNN has better anti-disturbance capacity and is suitable to the problem relate to food processing enterprise-alliance capacity based on questionnaire or interview.