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

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 system...

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Bibliographic Details
Main Authors: Rumchev, V.G., Swiniarski, R.W.
Format: Conference Proceeding
Language:English
Subjects:
Adaptive systems
Applied sciences
Artificial neural networks
Computer science
control theory
systems
Control system synthesis
Control theory. Systems
Exact sciences and technology
Linear systems
Mathematical model
Mathematics
Neural networks
Neurons
Optimal control
State-space methods
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Description
Summary: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.
ISSN:0743-1619
2378-5861
DOI:10.1109/ACC.2002.1025387