摘要
以前馈神经网络为研究对象，提出了一种容错型神经网络学习算法．将系统运行过程中可能发生的各类故障随机地引入网络训练过程，使系统获得更加稳健的内部表示．仿真结果表明，该学习算法能够有效地提高神经网络的容错能力和泛化能力．
A fault tolerance neural networks learning algorithm is proposed based on feedforward neural networks. The failures occur during system running are introduced randomly when network is trained. The result shows that a more robust internal representation is acquired by the system. The simulation result shows that this algorithm enhances the fault tolerant ability and the generalization of neural networks efficiently.
出处
《华中理工大学学报》
CSCD
北大核心
1998年第A02期22-24,共3页
Journal of Huazhong University of Science and Technology
