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System identification methods.png. The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. [1] System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction.
Sparse identification of nonlinear dynamics (SINDy) is a data-driven algorithm for obtaining dynamical systems from data. [1] Given a series of snapshots of a dynamical system and its corresponding time derivatives, SINDy performs a sparsity-promoting regression (such as LASSO) on a library of nonlinear candidate functions of the snapshots against the derivatives to find the governing equations.
System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The applications of system identification include any system where the inputs and outputs can be measured and include industrial processes, control systems, economic data, biology and the life sciences, medicine, social systems and many more.
The Eigensystem realization algorithm (ERA) is a system identification technique popular in civil engineering, in particular in structural health monitoring [citation needed]. ERA can be used as a modal analysis technique and generates a system realization using the time domain response (multi-)input and (multi-)output data. [1]
Gekko - simulation software in Python with machine learning and optimization; GNU Octave - an open-source mathematical modeling and simulation software very similar to using the same language as MATLAB and Freemat. JModelica.org is a free and open source software platform based on the Modelica modeling language.
Empirical gramians can be computed for linear and nonlinear control systems for purposes of model order reduction, uncertainty quantification or system identification. The emgr framework is a compact open source toolbox for gramian-based model reduction and compatible with OCTAVE and MATLAB.
Systems modeling or system modeling is the interdisciplinary study of the use of models to conceptualize and construct systems in business and IT development. [ 2 ] A common type of systems modeling is function modeling , with specific techniques such as the Functional Flow Block Diagram and IDEF0 .
JModelica.org is a commercial software platform based on the Modelica modeling language for modeling, simulating, optimizing and analyzing complex dynamic systems. [1] The platform is maintained and developed by Modelon AB in collaboration with academic and industrial institutions, notably Lund University and the Lund Center for Control of Complex Systems (LCCC). [2]