Ads
related to: matlab complete tutorial youtube
Search results
Results From The WOW.Com Content Network
MATLAB (an abbreviation of "MATrix LABoratory" [22]) is a proprietary multi-paradigm programming language and numeric computing environment developed by MathWorks.MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages.
ModelSim can also be used with MATLAB/Simulink, using Link for ModelSim. [ 7 ] [ 8 ] Link for ModelSim is a fast bidirectional co-simulation interface between Simulink and ModelSim. [ 8 ] [ 7 ] For such designs, MATLAB provides a numerical simulation toolset, while ModelSim provides tools to verify the hardware implementation & timing ...
OpenModelica Connection Editor [8] [9] is an open source graphical user interface for creating, editing and simulating Modelica models in textual and graphical modes. OMEdit communicates with OMC through an interactive API, requests model information and creates models/connection diagrams based on the Modelica annotations.
The conjugate gradient method can be derived from several different perspectives, including specialization of the conjugate direction method for optimization, and variation of the Arnoldi/Lanczos iteration for eigenvalue problems.
Rainflow counting identifies the closed cycles in a stress-strain curve. The rainflow-counting algorithm is used in calculating the fatigue life of a component in order to convert a loading sequence of varying stress into a set of constant amplitude stress reversals with equivalent fatigue damage.
AOL latest headlines, entertainment, sports, articles for business, health and world news.
VisualSim provides modeling libraries [9] for model-driven systems engineering activities. Libraries are used during the specification to optimize and validate the specification; during the hardware and software development phase to come up with the optimal architecture; and during the product debugging and testing phase to match the actual output with a set of expected results.
The primary application of the Levenberg–Marquardt algorithm is in the least-squares curve fitting problem: given a set of empirical pairs (,) of independent and dependent variables, find the parameters of the model curve (,) so that the sum of the squares of the deviations () is minimized: