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- W1993879423 abstract "Summary form only given, as follows. A novel methodology is examined for the fusion and propagation of uncertainties in a neural-network-like structure. Each node in the network represents a hypothesis. The inputs are the uncertainties associated with the knowledge sources that support the hypothesis, and the output is the aggregated uncertainty. The authors propose the use of fuzzy-set-based activation functions. The parameters of the activation function are chosen depending on the desired 'attitude'. In addition to the traditional union and intersection aggregation connectives, the authors propose the use of a generalized mean connective to increase flexibility. Some attractive properties of the connectives are discussed and a training procedure for such networks is proposed. Initial simulation results are also presented.< <ETX xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>></ETX>" @default.
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- W1993879423 date "1989-01-01" @default.
- W1993879423 modified "2023-09-25" @default.
- W1993879423 title "Neural networks for uncertainty management in vision systems" @default.
- W1993879423 doi "https://doi.org/10.1109/ijcnn.1989.118462" @default.
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