Bp Neural Network-based Internal Knowledge Transfer Effect Evaluation Studies

Knowledge management is viewed as the source of economy growth and an effective way of promoting knowledge-based economy development. Today, the activities of enterprise's knowledge transfer have already been the power and headspring that decide the subsistence and development of the enterprise. So, it is realistically significant for an enterprise to evaluate its knowledge transfer capacity objectively,scientifically, efficiently and quantitatively. Only in this way can an enterprise scientifically realize its knowledge management capacity in its competition with its counterpart, employ appropriate knowledge management strategy, make full use of its advantages and achieve the best economic and social benefits.The present paper makes an analysis of the enterprise's knowledge management capacity and sets up an index system based on the knowledge transfer process to evaluate the knowledge transfer capacity, and tries to find a way to synthesize it.In the first part, the author expounds the theories related to the knowledge transfer, introduces its concept and the influencing elements, and sets up an index system of evaluating the enterprise's knowledge transfer effects which is based on the knowledge transfer process according to former research. In the second part, because of the shortage of the evaluating the enterprise's knowledge transfer effects currently, the author presents a new method of evaluating knowledge transfer effects, which is based on BP neural network. According to the index system that has been already set up, design the BP neural network model, and present the doable evaluating programs. In the third part, on the method of compute, the author uses the Neural Network Toolbox (NNT) based on MATLAB to make network design and compute. Through the training and testing of parts of studying samples, the error of the model will limit in a preconcerted range. Thus, a integrative network model has been established. In the end, the article proves the accuracy feasibility of the method with an example.