Journal ArticleDOI
Belief Networks for Knowledge Integration and Abductive Inference in Fault Diagnosis of Chemical Processes
C. Rojas-Guzmán,Μ.A. Kramer +1 more
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TLDR
The use of probability theory as a framework to represent the uncertain elements of the diagnosis problem, and the use of distributed network (parallel) computations to determine the most probable diagnostic hypotheses are discussed.About:
This article is published in IFAC Proceedings Volumes.The article was published on 1992-04-01. It has received 2 citations till now. The article focuses on the topics: Legal expert system & Bayesian network.read more
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Intelligent systems in process engineering : A review
TL;DR: The purpose of this review is to sketch the directions that research and industrial applications of “intelligent systems” have taken in several areas of process engineering, and identify the emerging trends in each area, as well as the common threads that cut across several domains of inquiry.
Proceedings ArticleDOI
Remote diagnosis and monitoring of complex industrial systems using a genetic algorithm approach
C. Rojas-Guzman,M.A. Kramer +1 more
TL;DR: A novel technique to perform on-line remote monitoring and diagnosis of industrial and manufacturing systems based on Bayesian belief networks and genetic algorithms is described.
References
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Journal ArticleDOI
Local computations with probabilities on graphical structures and their application to expert systems
Journal ArticleDOI
Gaussian influence diagrams
TL;DR: This paper develops the framework for assessment and analysis of linear-quadratic-Gaussian models within the influence diagram representation, and provides algorithms to translate between the Gaussian influence diagram and covariance matrix representations for the normal distribution.
Journal ArticleDOI
Sensitivity Analysis in Bayesian Classification Models: Multiplicative Deviations
TL;DR: The sensitivity of Bayesian pattern recognition models to multiplicative deviations in the prior and conditional probabilities is investigated for the two-class case and results indicate that Bayesian systems which are based on limited data or subjective probabilities are expected to have a high percentage of correct classification despite the fact that the priorand conditional probabilities they use may deviate rather significantly from the true values.