用于神經(jīng)網(wǎng)絡(luò)容錯(cuò)的動(dòng)態(tài)冗余BP算法
A DYNAMIC REDUNDANCY BP ALGORITHM APPLIED IN THE FAULT TOLERANCE OF NEURAL NETWORKS
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摘要: 多層感知器(MLP)的容錯(cuò)性傳統(tǒng)上采用改進(jìn)算法和部件冗余方法。該文提出了一種動(dòng)態(tài)冗余BP算法,這種方法在傳統(tǒng)的帶沖量項(xiàng)的自適應(yīng)BP算法的學(xué)習(xí)過(guò)程中,根據(jù)各權(quán)值重要度的不同選取重要的權(quán)值進(jìn)行冗余處理。該算法能有效地提高網(wǎng)絡(luò)的容錯(cuò)能力,與學(xué)習(xí)中注入故障這一典型的容錯(cuò)改進(jìn)算法相比,盡管容錯(cuò)能力并不突出,但相對(duì)可節(jié)省大量的學(xué)習(xí)時(shí)間。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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