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The aim of this article is to provide a methodology useful for designing and controlling elastic computing self-organizing for artificial intelligence space exploration. The artificial intelligence application itself should be elastic and distributed in the context of limited information technologies resources in space. The most important use of elastic computing is artificial intelligence’s ability to continually learn and adapt to evolving environments and goals. The conceptual framework uses elastic infrastructure model and the terminology of graph dynamical systems to be able to capture a broad variety of processes taking place on self-organizing networks. The methodology’s uniqueness lies in the theory of graph dynamical systems used to explain the self-organizing processes life cycle.

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The article illustrates the prospects of using the hydrocuff technology of oscillations formation for evaluating hemodynamic parameters. An approach to improving the parameters accuracy estimation by increasing the oscillations amplitude and subsequent nonlinear SVD – filtering of the obtained blood pressure compression waveform based on singular spectral analysis is presented.

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