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Improving fault tolerance of a neural network is an important issue that has been studied for more than two decades. Various algorithms have been proposed in sequel and many of them have succeeded in attaining a fault tolerant neural network. Amongst all, on-line node fault injection-based algorithms are one type of these algorithms. Despite its simple implementation, theoretical analyses on these...
The `displacement expert' has recently proven popular for rapid tracking applications. In this paper, we note that experts are typically constrained only to produce approximately correct parameter updates at training locations. However, we show that incorporating constraints on the gradient of the displacement field within the learning framework results in an expert with better convergence and fewer...
This paper studied the accelerating convergence of the vector sequences generated by BP algorithm with vector epsilon algorithm, and presented the conclusion that the algorithms is not only convergent but also accelerated. Finally, we tested them for three classical artificial neural network problems. By numerical experiments, results shown that can reduce CPU time for computation and improve the...
The production period of the crystalline aluminium chloride is considerably long. However, the offline assay of AlCl3??6H2O content has large time delay. Thus soft sensor modeling is needed to analyze its content, and estimate the value to improve the product quality. The conventional back-propagation (BP) neural network training is easily trapped to the local minimum, To overcome this embarrassment,...
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