Download or read book Machine Learning Control by Symbolic Regression written by Askhat Diveev and published by Springer Nature. This book was released on 2021-10-23 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields. For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc. For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.

Download or read book Intelligent Control written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-07-03 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Intelligent Control The term "intelligent control" refers to a category of control methods that make use of a number of different artificial intelligence computing methodologies, including neural networks, Bayesian probability, fuzzy logic, machine learning, reinforcement learning, evolutionary computation, and genetic algorithms. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Intelligent Control Chapter 2: Artificial Intelligence Chapter 3: Machine Learning Chapter 4: Reinforcement Learning Chapter 5: Neural Network Chapter 6: Adaptive Control Chapter 7: Computational Intelligence Chapter 8: Outline of Artificial Intelligence Chapter 9: Machine Learning Control Chapter 10: Data-driven Model (II) Answering the public top questions about intelligent control. (III) Real world examples for the usage of intelligent control in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of intelligent control' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of intelligent control.

Download or read book Machine Learning Control Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2016-11-02 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Download or read book Machine Learning Control Taming Nonlinear Dynamics and Turbulence written by Thomas Duriez and published by Springer. This book was released on 2018-04-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of linear control theory are reviewed. In Chapter 4, MLC is shown to reproduce known optimal control laws for linear dynamics (LQR, LQG). In Chapter 5, MLC detects and exploits a strongly nonlinear actuation mechanism of a low-dimensional dynamical system when linear control methods are shown to fail. Experimental control demonstrations from a laminar shear-layer to turbulent boundary-layers are reviewed in Chapter 6, followed by general good practices for experiments in Chapter 7. The book concludes with an outlook on the vast future applications of MLC in Chapter 8. Matlab codes are provided for easy reproducibility of the presented results. The book includes interviews with leading researchers in turbulence control (S. Bagheri, B. Batten, M. Glauser, D. Williams) and machine learning (M. Schoenauer) for a broader perspective. All chapters have exercises and supplemental videos will be available through YouTube.

Download or read book High level Feedback Control With Neural Networks written by Young Ho Kim and published by World Scientific. This book was released on 1998-09-28 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively “add intelligence” to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for “high-level” neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including “dynamic output feedback”, “reinforcement learning” and “optimal design”, as well as a “fuzzy-logic reinforcement” controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.

Download or read book Machine Learning Applications to Robot Control written by Omar Abdul-hadi and published by . This book was released on 2018 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.

Download or read book Control Algorithm Acquistion Analysis and Reduction a Machine Learning Approach written by Katzberg, Jack David and published by Regina : Department of Computer Science, University of Regina. This book was released on 1988 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning Control A Complete Guide 2020 Edition written by Gerardus Blokdyk and published by 5starcooks. This book was released on 2020-01-23 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can you become the company that would put you out of business? What controls do you have in place to protect data? What sources do you use to gather information for a Machine learning control study? What is the smallest subset of the problem you can usefully solve? What management system can you use to leverage the Machine learning control experience, ideas, and concerns of the people closest to the work to be done? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Machine Learning Control investments work better. This Machine Learning Control All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Machine Learning Control Self-Assessment. Featuring 942 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Machine Learning Control improvements can be made. In using the questions you will be better able to: - diagnose Machine Learning Control projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Machine Learning Control and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Machine Learning Control Scorecard, you will develop a clear picture of which Machine Learning Control areas need attention. Your purchase includes access details to the Machine Learning Control self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Machine Learning Control Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

Download or read book Genetic Programming Theory and Practice II written by Una-May O'Reilly and published by Springer Science & Business Media. This book was released on 2006-03-16 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world problems. In order to facilitate these interactions, the number of talks and participants was small and the time for discussion was large. Further, participants were asked to review each other's chapters before the workshop. Those reviewer comments, as well as discussion at the workshop, are reflected in the chapters presented in this book. Additional information about the workshop, addendums to chapters, and a site for continuing discussions by participants and by others can be found at http://cscs.umich.edu:8000/GPTP-20041. We thank all the workshop participants for making the workshop an exciting and productive three days. In particular we thank all the authors, without whose hard work and creative talents, neither the workshop nor the book would be possible. We also thank our keynote speakers Lawrence ("Dave") Davis of NuTech Solutions, Inc., Jordan Pollack of Brandeis University, and Richard Lenski of Michigan State University, who delivered three thought-provoking speeches that inspired a great deal of discussion among the participants.

Download or read book Genetic Programming Theory and Practice IV written by Rick Riolo and published by Springer Science & Business Media. This book was released on 2007-07-03 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genetic Programming Theory and Practice IV was developed from the fourth workshop at the University of Michigan’s Center for the Study of Complex Systems. The workshop was convened in May 2006 to facilitate the exchange of ideas and information related to the rapidly advancing field of Genetic Programming (GP). The text explores the synergy between theory and practice, producing a comprehensive view of the state of the art in GP application.

Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Download or read book Machine Learning Proceedings 1992 written by Peter Edwards and published by Morgan Kaufmann. This book was released on 2014-06-28 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Proceedings 1992

Download or read book Genetic Programming Theory and Practice XII written by Rick Riolo and published by Springer. This book was released on 2015-06-04 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer function, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

Download or read book Computer Aided Systems Theory EUROCAST 2019 written by Roberto Moreno-Díaz and published by Springer Nature. This book was released on 2020-04-15 with total page 535 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 12013 and 12014 constitutes the thoroughly refereed proceedings of the 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019, held in Las Palmas de Gran Canaria, Spain, in February 2019. The 123 full papers presented were carefully reviewed and selected from 172 submissions. The papers are organized in the following topical sections: Part I: systems theory and applications; pioneers and landmarks in the development of information and communication technologies; stochastic models and applications to natural, social and technical systems; theory and applications of metaheuristic algorithms; model-based system design, verification and simulation. Part II: applications of signal processing technology; artificial intelligence and data mining for intelligent transportation systems and smart mobility; computer vision, machine learning for image analysis and applications; computer and systems based methods and electronic technologies in medicine; advances in biomedical signal and image processing; systems concepts and methods in touristic flows; systems in industrial robotics, automation and IoT.

Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Download or read book Genetic Programming written by Ting Hu and published by Springer Nature. This book was released on 2020-04-09 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.

Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.