Qualitative System Identification from Imperfect Data

Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the prec...

• Type: Journal
• Year: 2008
• Where: JAIR
• Authors: George Macleod Coghill, Ashwin Srinivasan, Ross D. King
• Keywords: JAIR 2008, Model, Qualitative Models, Quantitative Model