scholarly journals Observability of Uncertain Nonlinear Systems Using Interval Analysis

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 66 ◽  
Author(s):  
Thomas Paradowski ◽  
Sabine Lerch ◽  
Michelle Damaszek ◽  
Robert Dehnert ◽  
Bernd Tibken

In the field of control engineering, observability of uncertain nonlinear systems is often neglected and not examined. This is due to the complex analytical calculations required for the verification. Therefore, the aim of this work is to provide an algorithm which numerically analyzes the observability of nonlinear systems described by finite-dimensional, continuous-time sets of ordinary differential equations. The algorithm is based on definitions for distinguishability and local observability using a rank check from which conditions are deduced. The only requirements are the uncertain model equations of the system. Further, the methodology verifies observability of nonlinear systems on a given state space. In case that the state space is not fully observable, the algorithm provides the observable set of states. In addition, the results obtained by the algorithm allows insight into why the remaining states cannot be distinguished.

2004 ◽  
Vol 22 (2) ◽  
pp. 499-505
Author(s):  
Robert J. Elliott ◽  
Lakhdar Aggoun ◽  
Ali Benmerzouga

Author(s):  
Samuel P. Burns ◽  
Sabato Santaniello ◽  
William S. Anderson ◽  
Sridevi V. Sarma

Communication between specialized regions of the brain is a dynamic process allowing for different connections to accomplish different tasks. While the content of interregional communication is complex, the pattern of connectivity (i.e., which regions communicate) may lie in a lower dimensional state-space. In epilepsy, seizures elicit changes in connectivity, whose patterns shed insight into the nature of seizures and the seizure focus. We investigated connectivity in 3 patients by applying network-based analysis on multi-day subdural electrocorticographic recordings (ECoG). We found that (i) the network connectivity defines a finite set of brain states, (ii) seizures are characterized by a consistent progression of states, and (iii) the focus is isolated from surrounding regions at the seizure onset and becomes most connected in the network towards seizure termination. Our results suggest that a finite-dimensional state-space model may characterize the dynamics of the epileptic brain, and may ultimately be used to localize seizure foci.


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