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Artificial Neural Networks (ANNs) are widely used in computational and industrial applications. As technology is developed the scale of hardware is progressively becoming smaller and the number of faults is increasing. Therefore, fault-tolerant methods are necessary especially for ANNs used in critical applications. In this work, we propose a new method for fault-tolerant implementation of neural...
In a distributed system, process synchronization is an important agenda. One of the major duties for process synchronization is mutual exclusion. In new algorithm, opposite the past algorithms fairness happens. This paper presents a new approach of the race models involving distributed mutual exclusion. Further, concrete applications of these models did not involve variability in the accumulator size...
This paper proposes a hybrid learning scheme for modular-based recognizer for a problem of phoneme recognition. The scheme is established by combining two types of classifiers which are statistical and neural network-based ones. First, an initial modular topology is built employing statistical-based classifier and then, neural network-based classifiers are used as discriminators or local experts of...
This paper addresses the reliability of the bank note classifiers and a new method is proposed for improving the classification reliability based on the local principal components analysis (PCA). The reliability is evaluated by using an algorithm, which employs a function of winning class probability and second maximal probability in the LVQ classifier. The experimental results from 3,600 data samples...
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