MATLAB Machine Learning Recipes
Description
Harness the power of MATLAB to resolve a wide range of the latest machine learning challenges. The enhanced Fourth Edition is updated with new chapters on advanced machine learning techniques and real-world engineering applications.
This book offers broad appeal for anyone interested in machine learning, with a new introductory chapter on MATLAB for greater accessibility and a dedicated toolbox chapter to streamline modern workflows. It also demonstrates how MATLAB and machine learning can address challenges across various technical fields. New chapters cover key machine learning areas like pattern recognition, system identification, and computer vision, along with application-focused content on advanced engineering topics such as FPGA deployment, space missions, power electronics, and dynamic modeling.
Each example in the book addresses a real-world problem, with fully executable code provided for every solution. You can quickly find and follow step-by-step guidance for specific challenges, enabling you to leverage these technologies to build sophisticated applications in areas such as pattern recognition, autonomous driving, expert systems, and more.
What You Will Learn
- Write code for machine learning, adaptive control, and estimation using MATLAB
- Use MATLAB graphics and visualization tools for machine learning
- Gain knowledge of Kalman Filters
- Build neural networks, including physics-informed models
- Perform system identification and build expert systems
- Work with computer vision and autonomous systems
- Integrate neural networks to FPGA hardware
- Apply machine learning to remaining useful life prediction
Who This Book Is For
Software engineers, control engineers, university faculty, undergraduate and graduate students, hobbyists.
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Persons
Michael Paluszek is President of Princeton Satellite Systems, Inc. (PSS), which he founded in 1992 to provide aerospace consulting services. He developed the control system and simulations for the Indostar-1 satellite using MATLAB, leading to PSS's first commercial MATLAB product, the Spacecraft Control Toolbox, in 1995. Since then, he has created toolboxes and software for aircraft, submarines, robotics, and nuclear fusion propulsion, expanding PSS's product line. He leads the development of hypersonic aircraft, lunar landers, and deep space vehicles.
Previously, at GE Astro Space, he designed and led control systems for missions including GPS IIR, Inmarsat-3, Mars Observer, and the DMSP meteorological satellites, and participated in more than twelve satellite launches, including the GSTAR III recovery. At Draper Laboratory, he worked on navigation and control systems for the Space Shuttle, Space Station, and submarines. Michael holds a bachelor's in Electrical Engineering and master's and engineer's degrees in Aeronautics and Astronautics from MIT. He holds over a dozen U.S. patents and has authored numerous papers and books, including MATLAB Recipes, MATLAB Machine Learning, Practical MATLAB Deep Learning, and ADCS: Spacecraft Attitude Determination and Control Systems.
Layla Mohsen
is a software developer at PSS, specializing in machine learning and embedded systems. She holds a bachelor's degree in Computer Science from the American University in Cairo, where she graduated with highest honors. She was recognized among the top 10% of academic achievers in the School of Sciences and Engineering and received both the Academic Honor Award and a Certificate of Recognition for Research and Creativity. Layla designs and simulates a variety of aerospace and fusion energy systems. Her technical work at PSS includes predictive modeling for estimating the remaining useful life of power electronics for fusion plasma heating, optimizing computational time for particle-in-cell simulations, computational fluid dynamics of hypersonic inlets, and simulating a multi-stage induction coilgun system in MATLAB using the finite element method. She also developed the coilgun's control system and implemented laser communication for deep space emulation. Layla is passionate about building intelligent, energy-efficient systems optimized for real-time performance on low-power hardware, which she hopes to apply in meaningful projects that have a real-world impact.
Stephanie Thomas
holds bachelor's and master's degrees in Aeronautics and Astronautics from MIT. She has nearly 20 years of MATLAB experience, developing tools for solar sails, spacecraft proximity operations and launch vehicle analysis. She contributed to PSS's Attitude and Orbit Control textbook and authored numerous user guides. Stephanie has trained engineers globally and consulted for NASA, the Air Force, and the European Space Agency. She is a co-author of MATLAB Recipes, MATLAB Machine Learning, and Practical MATLAB Deep Learning Projects. In 2016, she was named a NASA NIAC Fellow for the 'Fusion-Enabled Pluto Orbiter and Lander' project. Stephanie is an Associate Fellow of AIAA and a member of its Propulsion and Energy Group.
Content
Part I.- 1. Basics.- 2. Toolboxes for Machine Learning.- 3. Graphics and Visualization.- Part 2.- 4. Overview.- 5. Kalman Filters.- 6. Adaptive Control.- 7. Fuzzy Logic.- 8. Simple Neural Nets.- 9. Classification with Neural Nets.- 10. Neural Nets with Deep Learning.- 11. Physics-informed Deep Neural Networks.- 12. Multiple Hypothesis Testing.- 13. System Identification.- 14. Computer Vision.- Part 3.- 15. Autonomous Driving with MHT.- 16. Spacecraft Attitude Determination.- 17. Neural Networks on FPGA from MATLAB.- 18. Position Determination for Landing.- 19. Remaining Useful Life.- 20. Neural Aircraft Control.- Appendix A. The History of Artificial Intelligence.- Appendix B. Software for Machine Learning.