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Optimization and State Estimation Fundamentals

Learn optimization fundamentals and state estimation techniques with this practical course!
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Content

Platform: Udemy
Video: 4h 36m
Language: English
Next start: On Demand

Table of contents

Description

This course covers the details of how to develop optimization and state estimation algorithms and apply them to real world practical applications. The course covers the following topics:
•Basic of system modeling which is how to describe any mechanical or electrical system in a mathematical form. •The theory of operation of Genetic Algorithm optimization which is extensively used in several industrial and academic applications •How to optimize parameters using experimental data•Implementation of Genetic algorithm logic in MATLAB environment and apply it to real world problems•How to represent systems in State space representation form. •Theory of operation of state estimation strategies such as Kalman Filtering •How to apply state estimation strategies such as Kalman filtering in MATLAB to real world problems.

You will learn

✓ Understand the theory of operation of Kalman filters and optimization strategies
✓ Estimate system states using Kalman Filters
✓ Extract parameters from data using optimization strategies
✓ Implement optimization and state estimation algorithms in MATLAB environment

Requirements

• Basic Mathematics background

This course is for

• For people who want to learn how to develop optimization/Estimation algorithms in MATLAB and Simulink
• For students who want to learn Genetic Algorithm optimization theory and practical implementation
• For students who want to learn Kalman filtering and state estimation strategies implementation
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business. 
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA. 
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.


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Platform: Udemy
Video: 4h 36m
Language: English
Next start: On Demand

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