A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayes's rule. Learning is regarded as collecting statistics ...
A probabilistic artificial neural network is presented. It is of a one-layer, feedback-coupled type with graded units. The learning rule is derived from Bayes's rule. Learning is regarded as collecting statistics and recall as a statistical inference process. Units correspond to events and connections come out as compatibility coefficients in a logarithmic combination rule. The input to a unit via connections from other active units affects the a posteriori belief in the event in question. The new model is compared to an earlier binary model with respect to storage capacity, noise tolerance, etc. in a content addressable memory (CAM) task. The new model is a real time network and some results on the reaction time for associative recall are given. The scaling of learning and relaxation operations is considered together with issues related to representation of information in one-layer artificial neural networks. An extension with complex units is discussed.
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential ...
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential equations are used as illustrative examples.
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