MATLAB/Simulink - Simulink Course for Electrical Engineering

Learn basics of MATLAB Simulink to simulate different electric components in MATLAB Simulink for electrical engineering

REDEEM

Description

"Complete MATLAB/Simulink - Simulink Course for Electrical Engineering"

The only course out there with everything you need to know about MATLAB Simulink from A to Z.

Throughout the course, you will:

Learn about numerical & symbolic computing using MATLAB.

Enhance your problem-solving skills in programming and building algorithms in MATLAB.

Implement MATLAB in your work and research. 

Create and manipulate Matrices which are the key to MATLAB programming.

Learn how to use MATLAB in some elementary mathematics problems.

Learn how to use MATLAB to produce 2D & 3D graphs.

Learn how to build 2D animations in MATLAB.

Learn how to use MATLAB as a programming language to build your own Algorithms.

Learn how to import and analyze data to MATLAB.

Get introduced to the symbolic capabilities of MATLAB.

You will also learn using MATLAB Simulink:

Simulation of basic electric circuits.

Simulation of operational amplifiers.

Simulation of charging and discharging of a capacitor.

Simulation of a source-free RL circuit.

Simulation of a source-free RC circuit.

Simulation of the step response of an RC circuit.

Simulation of the step response of an RL circuit.

Single-phase half-wave controlled rectifier.

Single-phase bridge controlled rectifier.

Single-phase AC chopper with R and RL load.

Buck regulator.

Boost regulator.

Buck-Boost regulator.

Single-phase half-bridge inverter.

Single-phase bridge inverter.

Three Phase Inverter.

PV cell in solar energy using Simulink tool in MATLAB.

How to obtain a complete grid-connected PV system in MATLAB.

You will learn about separately excited DC Machines and how to:

Model the DC machine in a no-load case using Simulink in MATLAB.

Model the DC machine in the presence of load torque using Simulink in MATLAB.

Simulating the DC machine using the power library from Simulink in MATLAB.

You will learn about Induction motors as:

Construction and principle of operation of induction motor.

Torque-speed characteristics of induction motor.

Equivalent circuit and power flow of induction motor.

Simulation of induction motor using Simulink in MATLAB.

MATLAB simulation of the wind turbine.

Cp plotting and lookup table in MATLAB.

MPPT in MATLAB Simulink.

Series resonant circuit in MATLAB.

Parallel resonant circuit in MATLAB.

Selection of PID parameters using an optimization algorithm such as PSO or particle swarm optimization algorithm.

After Taking This Course, You Will Be Able To Distinguish Yourself as a MATLAB User & Programmer

Take this course if you've been looking for ONE COURSE with in-depth insight into MATLAB Simulation.

Thank you, and hope to see you in our course for MATLAB :)

Who this course is for:

• High school and college students who want to learn MATLAB
• Researchers & engineers
• Anyone who wants to learn MATLAB
• Electrical and mechanical engineering students who want to learn about MATLAB

Getting Started with MATLAB Machine Learning

Getting Started with MATLAB Machine Learning, Easily extract patterns and knowledge from your data using MATLAB

• Created by Packt Publishing
• 2 hours on-demand video
• Full lifetime access
• Access on mobile and TV
• Certificate of Completion

What you'll learn

• Learn the introductory concepts of machine learning
• Explore the different types of regression technique such as simple and multiple linear regression, ordinary least squares estimation, correlations, and how to apply them to your data
• Discover the basics of classification methods and how to implement the Naive Bayes algorithm and Decision Trees in the MATLAB environment
• Perform data fitting, pattern recognition, and clustering analysis with the help of the MATLAB neural network toolbox

Description

MATLAB is the language of choice for many researchers and mathematics experts when it comes to machine learning. This video will help beginners build a foundation in machine learning using MATLAB. You'll start by getting your system ready with the MATLAB environment for machine learning and you'll see how to easily interact with the MATLAB workspace. You'll then move on to data cleansing, mining, and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll learn about the different types of regression technique and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction to improve performance. By the end of the video, you'll have learned to put it all together via real-world use cases covering the major machine learning algorithms and will be comfortable in performing machine learning with MATLAB.

About the Author

Giuseppe Ciaburro holds a Master's degree in chemical engineering from Università degli Studi di Napoli Federico II, and a Master's degree in acoustic and noise control from Seconda Università degli Studi di Napoli. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli."

He has over 15 years' work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in Python and R, and he has extensive experience of working with MATLAB. An expert in acoustics and noise control, Giuseppe has wide experience in teaching professional computer courses (about 15 years), dealing with e-learning as an author. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He is currently researching Machine Learning applications in acoustics and noise control.

Who is the target audience?

• This video is for data analysts, data scientists, students, or anyone keen to get started with machine learning and build efficient data processing and predictive applications.

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