We often encounter nonlinear dynamical systems that behave unpredictably, such as the earth's climate and the stock market. To analyze them, measurements taken over time are used to reconstruct the ...

Engineers have demonstrated a simple computational approach for supporting the classification performance of neural networks operating on sensor time series. The proposed technique involves feeding ...

Imagine standing on top of a mountain. From this vantage point, we can see picturesque valleys and majestic ridges below, and streams wind their way downhill. If a drop of rain falls somewhere on this ...

Example-oriented survey of nonlinear dynamical systems, including chaos. Combines numerical exploration of differential equations describing physical problems with analytic methods and geometric ...

Physicists and mathematicians at the University of Konstanz, ETH Zürich (Switzerland) and CNR INO in Trento (Italy) use concepts from topography to topologically classify and investigate ...

As a follow-on course to "Linear Kalman Filter Deep Dive", this course derives the steps of the extended Kalman filter and the sigma-point Kalman filter for estimating the state of nonlinear dynamic ...

Develop a foundation of analytical mechanics and multiphysics modeling. Use individual and team exercises to build skills for a dynamic systems approach. Engineered systems increasingly must exploit ...

Without internal work ( u = 0), convergence of automatic operations to controlled standards occurs only if the memory ...