摘要
用于训练神经网络的样本点集不可避免地会受到噪声污染。利用神经网络的概率描述,通过研究K—L信息距离和神经网络泛化能力的关系,构造一个新的神经网络学习误差函数。泛化能力分析和仿真结果表明了该学习误差函数的合理性。
The training data is inevitably contaminated with noise. At first the neural network is represented with a probability relation. Then the K_L information distance is adopted to be a learning error function, which keeps the consistence with the generalization. Finally the effectiveness of the new learning functions is proved through generalization analysis and the simulation example.
出处
《控制与决策》
EI
CSCD
北大核心
2000年第1期95-97,共3页
Control and Decision
基金
国家自然科学基金项目!(69874025)
关键词
神经网络
泛化能力
学习误差函数
概率表示
neural network, generalization, learning error function, probability representation, K_L information distance