Abstract
Multiaffine hybrid automata (MHA) represent a powerful formalism to model complex dynamical systems. This formalism is particularly suited for the representation of biological systems which often exhibit highly non-linear behavior. In this paper, we consider the problem of parameter identification for MHA. We present an abstraction of MHA based on linear hybrid automata, which can be analyzed by the SpaceEx model checker. This abstraction enables a precise handling of time-dependent properties. We demonstrate the potential of our approach on a model of a genetic regulatory network and a myocyte model.
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CITATION STYLE
Bogomolov, S., Schilling, C., Bartocci, E., Batt, G., Kong, H., & Grosu, R. (2015). Abstraction-based parameter synthesis for multiaffine systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9434, pp. 19–35). Springer Verlag. https://doi.org/10.1007/978-3-319-26287-1_2
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