Introduction to MatLAB

MatLAB is an Integrated Analysis tool suitable for various kind of Mathematical, statistical and data analytics operations in research. It also can be used for some aspects of Artificial Intelligence including design of Fuzzy Logic Controller and Programming.

Matlab has been described as a multi-paradigm numerical computing environment and is both suitable for statistical data analysis and programming.

Meanwhile, Simulink is a part of the MatLAB package the provides the tools you can use to simulate and test the models developed in MatLAB.


MatLAb Simulink
MatLAb Simulink GUI


Features of MatLAB

Features of MatLAB includes the following:

Programming: MatLAB could be used for writing programs which are saved in script files and then could be run in batches to perform numerical computation and analysis

Graphical User Interface: Rich graphical user interface of MatLAB makes it possible for various kinds of design and problem-solving scenarios.

Library of Math Tools: MatLAB provides a rich set of Math tools including functions for linear and polynomial algebra, mathematical statistics, linear programming, Calculus(differentiation and integration), Fourier series analysis, Laplace transforms and solutions to ODE.

Graphing and Images: You could visualize you data using tools provided by MatLAB just like you woul using any other spreadsheet application

Coding: Ease of coding is made possible by intellisense feature which reduces the time it takes to write codes by allowing resuse of already-used program blocks and codes.

Application Development: Interestingly, MatLab can be used to build application models

Integration: With MatLAB you can integrate with programming languages such as Java, C, C++ and .Net programming languages as well as MS Excel.

Machine Learning: MaLAB provides a rich set of toolkits for creating maching learning models for both supervised and unsupervises learning. Tools allow for classification, regresssion, clustering, PCA and more.



What MatLAB Can be Used for

  • Electronic and Signal Processing Applications
  • Computational Biology
  • Finantial Modelling and Analysis
  • Power and Control Systems
  • Image Recognition
  • Machine Learning
  • Production Engineering and Modelling


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