Matrix Laboratoryn (MATLAB) is a high-level language that is matrix based and includes an interactive environment for numerical computation, visualization,development and programming which was originally written in FROTRAN (Formual Translation). The present module is based on R 2017a version and is available for 64 bit Windows, Mac and Linux operating system. All the MATLAB variables used in MATLAB are stored in form of array. This article will explain the basic MATLAB interface and its useful tools.
Basic MATLAB interface
Following is the default interface that appears on starting the MATLAB software. This is divided into three windows i.e. command window, workspace window and current folder window.
- Command window allows commands to be executed are written and results from the executed commands are also displayed.
- Workspace window displays the variables to be used.the variables used for analysis are displayed.
- Current folder window displays the files displayed and the researcher can access same.
Understanding the quick access toolbar
Further, the MATLAB interface consists of a toolbar on top of the window called “Quick Access Toolbar” consisting of three tabs namely “Home”, “Plots” and “Apps”. These are called Global Tabs. The figures below show the home, plot and app tab respectively.
Global Tabs and their basic features
|Global Tabs||The Home Tab||The Plot Tab||The App Tab|
|Objective||Provides access to basic and frequently used commands||Provides access to various plots and graphs||Provides access to frequently apps having higher and complex functionality|
|Features||1. To open a new,existing document documents.
2. Import and compare content on files
3. Open and create variables
4. Run and analyze code
5. Clear code and command
6. Add-ons like; set-path, parallel computing
7. Support and help
|Presents all the available plots in MATLAB including line plots, stem and stair plots, bar plots and scatter plots.||1. Curve Fitting for fitting the curve and surfaces to data.
2. Optimization app to setup and solve optimization problems
3. Signal Analyzer to view, analyzes and compare signals.
4. Image Acquisition to acquires images and videos from hardware.
5. Instrument Control to Control instruments like oscilloscope.
6. SimBiology to model and simulate biological systems.
7. Classification Learner to trains models which will classify data.
8. Distribution Filter to fit probability distribution to data.
9. Neural Net Cluttering to solve clustering problems using networks.
10. Neural Net Pattern Recognition to solve pattern recognition problem using feed forward networks.
11. Neural Net Time Series to solve nonlinear time series problems
Table – Description of Quick Access Toolbar
The next article will explain in detail about the workspace window where one can add different types of variables.
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