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
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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.
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