Observability of Complex Systems: Finding the Gap. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Stigter, J.; Joubert, D.; Molenaar, J.
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 9
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-16682-x
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract For a reconstruction of state and parameter values in a dynamic system model, first the question whether these values can beuniquely determined from the data must be answered. This structural model property is known as observability or, in case of parameter calibration only, identifiability . Testing a given model for observability is a well studied problem in the systems and control sciences. However, it is increasingly difficult, if not impossible, to address this property for large size models that, nowadays, are frequently used. We demonstrate the application of a recently developed algorithm that overcomes this problem and is remarkably efficient. As an illustration we show how an observability analysis for a Chinese Hamster Ovary Cell model (34 states, 117 parameters), a JAKSTAT signalling model (31 states, 51 parameters), and a MAP Kinase model (100 states, 88 parameters) can be established in a very short time.