Properties and learning algorithms for faulty RBF networks with coexistence of weight and node failures

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• 作者：Rui-Bin FengAuthor Vitae ; Zi-Fa HanAuthor Vitae ; Wai Yan WanAuthor Vitae ; Chi-Sing LeungAuthor Vitae
• 关键词：RBF networks ; Fault tolerance ; Weight failure ; Node fault ; Weight noise ; Weight fault
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：8 February 2017
• 年：2017
• 卷：224
• 期：Complete
• 页码：166-176
• 全文大小：2122 K
• 卷排序：224

文摘

Although there are many fault tolerant algorithms for neural networks, they usually focus on one kind of weight failure or node failure only. This paper first proposes a unified fault model for describing the concurrent weight and node failure situation, where open weight fault, open node fault, weight noise, and node noise could happen in a network at the same time. Afterwards, we analyze the training set error of radial basis function (RBF) networks under the concurrent weight and node failure situation. Based on the finding, we define an objective function for tolerating the concurrent weight and node failure situation. We then develop two learning algorithms, one for batch mode learning and one for online mode learning. Furthermore, for the online mode learning, we derive the convergent conditions for two cases, fixed learning rate and adaptive learning rate.