Propagation Of Uncertainty Using Neural Networks

Raghu J. Krishnapuram; Joonwhoan Lee

doi:10.1117/12.960298

27 March 1989 Propagation Of Uncertainty Using Neural Networks

Raghu J. Krishnapuram, Joonwhoan Lee

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.

Citation Download Citation

Raghu J. Krishnapuram and Joonwhoan Lee "Propagation Of Uncertainty Using Neural Networks", Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); https://doi.org/10.1117/12.960298

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available