Title :
The Evaluation Model of Knowledge Management Based on Information Entropy and RBF Neural Network (IE-RBF)
Author_Institution :
Dept. of Comput. & Modern Educ. Technol., Chongqing Educ. Coll., Chongqing, China
Abstract :
Knowledge management is a complex systems engineering, so the evaluation of knowledge management is of non-linear characteristics. Neural network with the ability of adaptive learning is an excellent tool to deal with the issue of non-linear. This paper analyzed the essence of knowledge and knowledge management. We proposed an evaluation model of knowledge management based on the theory of information entropy and RBF neural network. After reduction of the indices system with information entropy to reduce, we would evaluate the knowledge management with RBF neural network. After empirical research with MATLAB7.0, it is had been proved that the method is validity and practicality. And then it did not only overcome the traditional methodspsila shortcoming which is too subjective, but also avoided complex process of the traditional evaluation method.
Keywords :
entropy; knowledge management; learning (artificial intelligence); radial basis function networks; RBF neural network; adaptive learning; evaluation model; information entropy; knowledge management; nonlinear characteristic; radial basis function network; Computer networks; Computer science education; Information entropy; Information management; Information processing; Information theory; Knowledge management; Mathematical model; Neural networks; Systems engineering education; Information Entropy; Knowledge Management; Neural Network;
Conference_Titel :
Information Processing, 2009. APCIP 2009. Asia-Pacific Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-0-7695-3699-6
DOI :
10.1109/APCIP.2009.10