Evaluation of Independent Innovation Ability of SMEs Based on Neuro-Fuzzy Decision Tree

Enhancing the ability of autonomous innovation for medium-and-small enterprises have the important strategic sense to upgrade the competitiveness of enterprises. Establishing an effective evaluation system and model can provide a theoretical basis and analytical tools for medium-and-small enterprises to understand the current situation and problems. So, in this paper, neuro-fuzzy decision tree (neuro-FDT) is introduced to the research on the independable innovation enterprises . Fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are very poor in classification accuracy. Neural networks-fuzzy decision tree improves FDT´s classification accuracy and extracts more accuracy human interpretable classification rules. The fuzzy rules enable a decision-maker to decide the independable innovation ability of enterprises. Comparing with the conventional methods, the mathematical model of neuro-fuzzy decision tree can be easily established by fully utilizing the information of enterprises. The result of the positive research indicated that this mathematical model is very valid for independable innovation ability evaluation and it will have a good application prospect in this area.