Knowledge Extension and Fault Recovery of Feed Forward Neural Networks
前馈网的知识扩充及故障恢复

Aiming at the problem of poor extensibility of feed forward neural networks,a knowledge extension method is proposed in this paper.Preserving the original neural networks,we can both retain existing training result and learn new knowledge by adding a new subnet.Simultaneously,the strategy of fault recovery of neural networks is studied and a fault compensation algorithm is given.The effectiveness of proposed algorithms is verified by numerical simulations.