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A Novel Modeling Methodology: Generalized Nets

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Artificial Intelligence and Soft Computing – ICAISC 2006 (ICAISC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4029))

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Abstract

The generalized net methodology was developed as a counterpart of Petri nets. The methodology allows to model different kinds of discrete dynamic systems. The basics of the theory of generalized nets is introduced and next the algorithm of generalized nets is described. Algebraic aspects of generalized nets as well as operator aspects of generalized nets are described. At the end, one possible application of generalized nets, namely for neural networks is shown. Here a neural network without any aggregation is considered.

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References

  1. Atanassov, K.: Generalized nets. World Scientific, Singapore, New Jersey, London (1991)

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  2. Atanassov, K.: Generalized Nets and Systems Theory, Prof. M. Drinov. Academic Publishing House, Sofia (1997)

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  3. Krawczak, M.: Multilayer Neural Systems and Generalized Net Models. Academic Press EXIT, Warsaw (2003)

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  4. Radeva, V., Krawczak, M., Choy, E.: Review and Bibliography on Generalized Nets Theory and Applications. Advanced Studies in Contemporary Mathematics 4, 173–199 (2002)

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© 2006 Springer-Verlag Berlin Heidelberg

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Krawczak, M. (2006). A Novel Modeling Methodology: Generalized Nets. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2006. ICAISC 2006. Lecture Notes in Computer Science(), vol 4029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11785231_121

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  • DOI: https://doi.org/10.1007/11785231_121

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35748-3

  • Online ISBN: 978-3-540-35750-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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