Diagnostics of dynamical systems by recognizing the default and abnormal pattern

Diagnostic problems are proposed to be solved via the pattern recognition approach. The main example has been motivated by economic macro modelling, hence a reference model approach is adopted. The dynamic systems under consideration are assumed to be linear in three case studies and nonlinear in one case study. All four case studies illustrate the basic concept of this approach. Since the state vector represents the most compact information about the dynamic system, a measured/estimated state vector is chosen as the feature vector of a specific pattern vector. The results obtained verify our belief that this approach can be very useful if the assumption of linearity has been met. It is worthwhile to point out that although this approach appears fairly straightforward, it is quite powerful since computational power is now readily available. Since information systems with large databases are readily available, we believe the approach presented in the paper are certainly practical. The first case study deals with a macro economic model, while the second deals with a three state variable linear difference equation model. The third example illustrates use of patterns in frequency domain to make inference of a dynamic system diagnostic in time domain. Finally, the investigation focuses on the simulation of a nonlinear system. It is quite certain to conclude that the nonlinear system behavior is very dependent and sensitive to parameter changes.