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Modeling the reachable sets for positive linear systems using self-regulating adaptive perceptron type neural networks | IEEE Conference Publication | IEEE Xplore

Modeling the reachable sets for positive linear systems using self-regulating adaptive perceptron type neural networks


Abstract:

The paper presents a technique for modeling reachable states of positive linear discrete-time systems (PLDS) using static feed-forward neural networks. The proposed metho...Show More

Abstract:

The paper presents a technique for modeling reachable states of positive linear discrete-time systems (PLDS) using static feed-forward neural networks. The proposed method is based on design of self-regulating two layer perceptron type neural network for the modeling of reachable sets of PLDS systems represented by polyhedral cones using a pattern recognition method.
Date of Conference: 08-10 May 2002
Date Added to IEEE Xplore: 07 November 2002
Print ISBN:0-7803-7298-0
Print ISSN: 0743-1619
Conference Location: Anchorage, AK, USA

References

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