Abstract
Some definitions and theorems concerning positive continuous-time and discrete-time linear systems are presented. The notion of a positive estimator maping a positive cone into a positive cone is introduced. A multi-layer perceptron and a radial neural network approximating the nonlinear estimator are proposed.
A neural network modeling the dynamics of a positive nonlinear dynamical system is also proposed. The new neural networks are verified and illustrated by an example.
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© 2004 Springer-Verlag Berlin Heidelberg
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Kaczorek, T. (2004). Neural Networks of Positive Systems. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_8
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DOI: https://doi.org/10.1007/978-3-540-24844-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22123-4
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