Model Reduction Methods for Complex Network Systems

Model reduction methods for chaotic systems

Judging model reduction of complex systems.

Model reduction is a common goal in the study of complex systems, consisting of many components with a complex interaction structure. The quality of such reduction, however, may not be reflected correctly in the stepwise prediction error in the model since it ignores the global geometry of the dynamics. Here we introduce a general two-step framework, consisting of dimensionality reduction of th...

Requirements Engineering Model in Designing Complex Systems

This research tends to development of the requirements elicitation methodology with regard to operational nature and hierarchical analysis for complex systems and also, regarding available technologies. This methodology applies Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) to ensure traceability of planned qualitative and quantitative data from requirements to available te...

Requirements Engineering Model in Designing Complex Systems

This research tends to development of the requirements elicitation methodology with regard to operational nature and hierarchical analysis for complex systems and also, regarding available technologies. This methodology applies Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) to ensure traceability of planned qualitative and quantitative data from requirements to available te...

Automated Model Reduction for Complex Systems Exhibiting Metastability

We present a novel method for the identification of the most important metastable states of a system with complicated dynamical behavior from time series information. The novel approach represents the effective dynamics of the full system by a Markov jump process between metastable states, and the dynamics within each of these metastable states by rather simple stochastic differential equations...