27 March 1989 Propagation Of Uncertainty Using Neural Networks
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Proceedings Volume 1002, Intelligent Robots and Computer Vision VII; (1989) https://doi.org/10.1117/12.960298
Event: 1988 Cambridge Symposium on Advances in Intelligent Robotics Systems, 1988, Boston, MA, United States
Abstract
Uncertainty management (belief maintenance) is an important part of most knowledge-based computer vision systems. In this paper, we examine a methodology to aggregate and propagate 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 activation function is selected based on concepts from fuzzy set theory. The parameters of the activation function are chosen depending on the type of aggregation required. In addition to the traditional union and intersection aggregation connectives, we 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 also proposed.
© (1989) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Raghu J. Krishnapuram, Raghu J. Krishnapuram, Joonwhoan Lee, Joonwhoan Lee, } "Propagation Of Uncertainty Using Neural Networks", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960298; https://doi.org/10.1117/12.960298
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