用于神经网络容错的动态冗余BP算法
A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS
-
摘要: 多层感知器(MLP)的容错性传统上采用改进算法和部件冗余方法。该文提出了一种动态冗余BP算法,这种方法在传统的带冲量项的自适应BP算法的学习过程中,根据各权值重要度的不同选取重要的权值进行冗余处理。该算法能有效地提高网络的容错能力,与学习中注入故障这一典型的容错改进算法相比,尽管容错能力并不突出,但相对可节省大量的学习时间。Abstract: There are two main types of approaches in the research of fault tolerance of Multilayer Perceptrons(MLP): improvement in the learning algorithm and component redundancy after training. A dynamic redundancy BP algorithm is presented. In the training steps of the conventional adaptive BP algorithm with a momentum term, the most important weights are replicated based on their significance. Applying the algorithm the fault tolerance of a network can be improved effectively. Compared with fault injection while training--a typical improved learning algorithm, although this dynamic redundancy algorithm gives no prominence in fault tolerance, the training time can be greatly reduced.
-
A.F. Murray, P. J. Edwards, Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training, IEEE Trans. on Neural Networks, 1994, NN-5(5), 792802.[2]P. Kerlinzin, P. Refregier, Theoretical investigation of the robustness of multilayer perceptrons,Analysis of the linear case and extension to nonlinear networks, IEEE Trans. on Neural Networks.1995, NN-6(3), 560-571.[3]B.S. Arad, A. El-Anway, On fault tolerant training of feedforward neural networks, Neural Networks, 1997, 10(3), 539-557.[4]D.S. Phatak, I. Koren, Complete and partial fault tolerance of feedforward neural nets, IEEE Trans. on Neural Networks, 1995, NN-6(2), 446-456.[5]D.S. Phatak, I. Koren, Fault tolerance of feedforward neural nets for classification tasks, IJCNN,Nagoya, Japan, 1992, II-386-391.[6]Xu Liqin.[J].Hu Dongcheng, A new component redundancy method in MLPs fault tolerance, ICEMI99, Harbin, China.1999,:- -
计量
- 文章访问数: 2002
- HTML全文浏览量: 109
- PDF下载量: 426
- 被引次数: 0
下载:
