Fuzzy decision neural networks and application to data fusion

A decision-based neural network (DBNN) is extended to a fuzzy-decision neural network (FDNN), which is shown to offer classification/generalization performance improvements, especially when the data are not clearly separable. The hierarchical structure adopted make the computation process very efficient. The learning rule and some key properties of FDNN are described. A Bayesian paradigm offers an optimal approach to data fusion. This approach is explored. DBNN, together with a Bayesian approach, is proposed to formulate the data fusion process.<
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