54,980 research outputs found
A probabilistic model for information and sensor validation
This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model
Probabilistic Dynamic Logic of Phenomena and Cognition
The purpose of this paper is to develop further the main concepts of
Phenomena Dynamic Logic (P-DL) and Cognitive Dynamic Logic (C-DL), presented in
the previous paper. The specific character of these logics is in matching
vagueness or fuzziness of similarity measures to the uncertainty of models.
These logics are based on the following fundamental notions: generality
relation, uncertainty relation, simplicity relation, similarity maximization
problem with empirical content and enhancement (learning) operator. We develop
these notions in terms of logic and probability and developed a Probabilistic
Dynamic Logic of Phenomena and Cognition (P-DL-PC) that relates to the scope of
probabilistic models of brain. In our research the effectiveness of suggested
formalization is demonstrated by approximation of the expert model of breast
cancer diagnostic decisions. The P-DL-PC logic was previously successfully
applied to solving many practical tasks and also for modelling of some
cognitive processes.Comment: 6 pages, WCCI 2010 IEEE World Congress on Computational Intelligence
July, 18-23, 2010 - CCIB, Barcelona, Spain, IJCNN, IEEE Catalog Number:
CFP1OUS-DVD, ISBN: 978-1-4244-6917-8, pp. 3361-336
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