Research on Tacit Knowledge Acquisition Based on Gray Theory and RBF Neural Network

In the era of knowledge economy, Knowledge has become the main means of production and the core source for value creation. However, especially tacit knowledge acquisition, knowledge acquisition is bottleneck in all kinds of knowledge system, cutting down the benefit of knowledge sharing, application and innovation. In view of this, in this paper, the main ideas and deficiencies of the existing methods of knowledge acquisition are generalized. And then, the basic principles, characteristics and the integrated advantage of gray theory and RBF neural networks are analyzed. On this basis, three kinds of models are designed and discussed, which is able to obtain tacit rule set by using gray theory integrated with RBF neural network. Simultaneously, an empirical analysis is carried out to analyze application results of tacit knowledge acquisition, which includes five kinds of models using independent and integrated strategies.