Abstract
We discuss a fault-tolerance of multilayer perceptrons in which input and output learning examples are patterns consisting of 0s and 1s. A type of faults to be dealt with is a multiple neuron and/or weight fault where neurons are in the hidden layer and weights are between the hidden and output layers. We theoretically analyze the condition when a multilayer perceptron is tolerant to multiple neuron and weight faults. According to the analysis, we propose two value injection methods denoted as VIM-WN and VIM-N to make multilayer perceptrons tolerant to all multiple neuron and/or weight faults whose values are in a multi-dimensional interval. In VIM-WN, the extreme values specified by the fault ranges are set to the outputs of the selected neurons and the selected weights of the links at the same time in a learning phase. In VIM-N, the extreme values specified by the fault ranges are set only to the outputs of the selected neurons likewise. First, we present an algorithm based on VIM-WN and prove that a multilayer perceptron which has successfully finished learning by VIM-MN is tolerant to all multiple neuron-and-weight faults whose values are in the interval, under the condition that the multiplicity of the multiple fault is within a certain number specified by faulty neurons and weights. Next, we present them concerning VIM-N likewise. By simulation, we confirm the analytical results for VIM-WN and VIM-N. We also by simulation examine the degrees of fault tolerance concerning multiple neuron-and-weight faults for VIM-N and VIM-W where VIM-W is the method proposed in [1] and show that VIM-N and WIM-W as well as VIM-WN are almost equally effective in coping with multiple neuron-and-weight faults. In addition, we show the data in terms of the learning time, successful rate of learning.
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Horita, T., Takanami, I., Nishimura, K. (2014). Multilayer Perceptrons Which Are Tolerant to Multiple Faults and Learnings to Realize Them. In: Gavrilova, M.L., Tan, C.J.K. (eds) Transactions on Computational Science XXII. Lecture Notes in Computer Science, vol 8360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54212-1_3
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