Let be the strings of fuzzy sets over , where is a finite universe of discourse. We present the algorithms for operations on fuzzy sets and the polynomial time algorithms to find the string over which is a closest common subsequence of fuzzy sets of and using different operations to measure a similarity of fuzzy sets.
Bayesian probability theory provides a framework for data modeling. In this framework it is possible to find models that are well-matched to the data, and to use these models to make nearly optimal predictions. In connection to neural networks and especially to neural network learning, the theory is interpreted as an inference of the most probable parameters for the model and the given training data. This article describes an application of Neural Networks using the Bayesian training to the problem...
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