Search results
Results from the WOW.Com Content Network
Dormand–Prince is the default method in the ode45 solver for MATLAB [4] and GNU Octave [5] and is the default choice for the Simulink's model explorer solver. It is an option in Python's SciPy ODE integration library [6] and in Julia's ODE solvers library. [7]
In mathematics, the Runge–Kutta–Fehlberg method (or Fehlberg method) is an algorithm in numerical analysis for the numerical solution of ordinary differential equations. It was developed by the German mathematician Erwin Fehlberg and is based on the large class of Runge–Kutta methods.
A Tutorial on Independent Component Analysis; FastICA as a package for Matlab, in R language, C++; ICALAB Toolboxes for Matlab, developed at RIKEN; High Performance Signal Analysis Toolkit provides C++ implementations of FastICA and Infomax; ICA toolbox Matlab tools for ICA with Bell-Sejnowski, Molgedey-Schuster and mean field ICA. Developed at ...
Simulink is a MATLAB-based graphical programming environment for modeling, simulating and analyzing multidomain dynamical systems. Its primary interface is a graphical block diagramming tool and a customizable set of block libraries. It offers tight integration with the rest of the MATLAB environment and can either drive MATLAB or be scripted ...
The first time-stepping integrator was introduced by J.J. Moreau. The integrators do not aim at resolving switching points and are therefore very robust in application. As the integrators work with the integral of the contact forces and not with the forces itself, the methods can handle both motion and impulsive events like impacts.
Model-based design (MBD) is a mathematical and visual method of addressing problems associated with designing complex control, [1] signal processing [2] and communication systems. It is used in many motion control , industrial equipment, aerospace , and automotive applications.
Get AOL Mail for FREE! Manage your email like never before with travel, photo & document views. Personalize your inbox with themes & tabs. You've Got Mail!
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.