A neural network diagnosis model without disorder independence assumption
Generally, the disorders in a neural network diagnosis model are assumed independent each other. In this paper, we propose a neural network model for diagnostic problem solving where the disorder independence assumption is no longer necessary. Firstly, we characterize the diagnostic tasks and the causal network which is used to represent the diagnostic problem, then we describe the neural network diagnosis model, finally, some experiment results will be given.
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