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In mathematics and science, a nonlinear system (or a non-linear system) is a system in which the change of the output is not proportional to the change of the input. [1] [2] Nonlinear problems are of interest to engineers, biologists, [3] [4] [5] physicists, [6] [7] mathematicians, and many other scientists since most systems are inherently nonlinear in nature. [8]
Dynamical neuroscience describes the non-linear dynamics at many levels of the brain from single neural cells [3] to cognitive processes, sleep states and the behavior of neurons in large-scale neuronal simulation. [4] Neurons have been modeled as nonlinear systems for decades, but dynamical systems are not constrained to neurons.
However, real-world systems are often nonlinear and multidimensional, in some instances rendering explicit equation-based modeling problematic. Empirical models, which infer patterns and associations from the data instead of using hypothesized equations, represent a natural and flexible framework for modeling complex dynamics.
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Non-Linear Dynamics Group, Instituto Superior Técnico, Technical University of Lisbon; Dynamical Systems Archived 2017-06-02 at the Wayback Machine, IMPA, Instituto Nacional de Matemática Pura e Applicada. Nonlinear Dynamics Workgroup Archived 2015-01-21 at the Wayback Machine, Institute of Computer Science, Czech Academy of Sciences.
Ali Hasan Nayfeh (Arabic: علي نايفة) (21 December 1933 – 27 March 2017) [1] was a Palestinian-Jordanian mathematician, mechanical engineer and physicist. [2] He is regarded as the most influential scholar and scientist in the area of applied nonlinear dynamics in mechanics and engineering. [3]
The method removes secular terms—terms growing without bound—arising in the straightforward application of perturbation theory to weakly nonlinear problems with finite oscillatory solutions. [1] [2] The method is named after Henri Poincaré, [3] and Anders Lindstedt. [4]
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.