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浏览/检索结果: 共1条，第1-1条 帮助 已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 A new early stopping algorithm for improving neural network generalization (EI CONFERENCE) 会议论文 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, October 10, 2009 - October 11, 2009, Changsha, Hunan, China Wu X.-X.; Liu J.-G. 收藏  |  浏览/下载：19/0  |  提交时间：2013/03/25 As generalization ability of neural network was restricted by overfitting problem in the network's training. Early stopping algorithm based on fuzzy clustering was put forward to solve this problem in this paper. Subtractive clustering and Fuzzy C-Means clustering (FCM) were combined to realize optimal division of training set  validation set and test set. How to realize this algorithm in backpropagation (BP) network by utilizing neural network toolbox and fuzzy logic toolbox in MATLAB was dwelled on. Early stopping algorithm based on fuzzy clustering and other early stopping algorithms were applied in function approximation and pattern recognition problems in validation experiments. Experiments results indicate that early stopping algorithm based on fuzzy clustering has higher precision in comparison to other early stopping algorithms. Outputs of training set  validation set and test set are more accordant. 2009 IEEE.