摘要
In order to improve generalization capability of neural networks, a model structure of Case-Based neural networks has been presented. The model blended Case-Based Reasoning method into neural networks and has the ability of incrementally learning. The results demonstrated that the model could observably improve the generalization capability of supervised neural networks. Firstly, paper summarized the advancing front of researching on generalization capability of neural networks.Secondly, the structure of CBNN and its process of working were introduced. Finally, the results of experiments were compared and discussed.
In order to improve generalization capability of neural networks, a model structure of Case-Based neural networks has been presented. The model blended Case-Based Reasoning method into neural networks and has the ability of incrementally learning. The results demonstrated that the model could observably improve the generalization capability of supervised neural networks. Firstly, paper summarized the advancing front of researching on generalization capability of neural networks. Secondly, the structure of CBNN and its process of working were introduced. Finally, the results of experiments were compared and discussed.
出处
《数值计算与计算机应用》
CSCD
北大核心
2004年第1期65-73,共9页
Journal on Numerical Methods and Computer Applications
