Edited by Timothy Sands Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of Deterministic Artificial Intelligence the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in Deterministic Artificial rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial Intelligence intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal Edited by Timothy Sands learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book. ISBN 978-1-78984-111-4 Published in London, UK © 2020 IntechOpen © Quardia / iStock Deterministic Artificial Intelligence Edited by Timothy Sands Published in London, United Kingdom Supporting open minds since 2005 Deterministic Artificial Intelligence http://dx.doi.org/10.5772/intechopen.81309 Edited by Timothy Sands Contributors Matthew Cooper, Kyle Baker, Emmanuel Oyekanlu, Azura Che Soh, Behzad Vaferi, Raheni T D, P Thirumoorthi, Tatiana Mikhaylovna Zubkova, Brendon Smeresky, Alexa Rizzo © The Editor(s) and the Author(s) 2020 The rights of the editor(s) and the author(s) have been asserted in accordance with the Copyright, Designs and Patents Act 1988. All rights to the book as a whole are reserved by INTECHOPEN LIMITED. The book as a whole (compilation) cannot be reproduced, distributed or used for commercial or non-commercial purposes without INTECHOPEN LIMITED’s written permission. 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The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. First published in London, United Kingdom, 2020 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 7th floor, 10 Lower Thames Street, London, EC3R 6AF, United Kingdom Printed in Croatia British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library Additional hard and PDF copies can be obtained from [email protected] Deterministic Artificial Intelligence Edited by Timothy Sands p. cm. Print ISBN 978-1-78984-111-4 Online ISBN 978-1-78984-112-1 eBook (PDF) ISBN 978-1-83880-728-3 An electronic version of this book is freely available, thanks to the support of libraries working with Knowledge Unlatched. 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For more information visit www.intechopen.com Meet the editor Dr. Timothy Sands graduated from Columbia University, Stan- ford University, and the Naval Postgraduate School. He is an International Scholar Laureate of the Golden Key International Honor Society, a Fellow of the Defense Advanced Research Projects Agency, a panelist of the National Science Foundation Graduate Research Fellowship program, and an interviewer for undergraduate admissions at Stanford University. He has published prolifically in archival journals, conference proceedings, books, and book chapters, in addition to giving plenary, keynote, and invitational presentations; he holds one patent in spacecraft attitude control. He is currently the Associate Dean of the Naval Postgraduate School’s Graduate School of Engineering and Applied Science having previously served as a university chief academic officer, dean, and research center director. Contents Preface III Section 1 Stochastic Approaches 1 Chapter 1 3 Stochastic Artificial Intelligence: Review Article by T.D. Raheni and P. Thirumoorthi Chapter 2 23 Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System by Ahmed Abdulelah Ahmed, Azura Che Soh, Mohd Khair Hassan, Samsul Bahari Mohd Noor and Hafiz Rashidi Harun Chapter 3 49 Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band by Emmanuel Oyekanlu and Jia Uddin Chapter 4 69 Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids by Behzad Vaferi Chapter 5 99 The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements by Tatyana Zubkova and Marina Tokareva Section 2 Deterministic Approaches 117 Chapter 6 119 Deterministic Approaches to Transient Trajectory Generation by Matthew A. Cooper Contents Preface XIII Section 1 Stochastic Approaches 1 Chapter 1 3 Stochastic Artificial Intelligence: Review Article by T.D. Raheni and P. Thirumoorthi Chapter 2 23 Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System by Ahmed Abdulelah Ahmed, Azura Che Soh, Mohd Khair Hassan, Samsul Bahari Mohd Noor and Hafiz Rashidi Harun Chapter 3 49 Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Prediction in the Powerline Narrowband Frequency Band by Emmanuel Oyekanlu and Jia Uddin Chapter 4 69 Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids by Behzad Vaferi Chapter 5 99 The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements by Tatyana Zubkova and Marina Tokareva Section 2 Deterministic Approaches 117 Chapter 6 119 Deterministic Approaches to Transient Trajectory Generation by Matthew A. Cooper Chapter 7 141 Sinusoidal Trajectory Generation Methods for Spacecraft Preface Feedforward Control by Kyle A. Baker Chapter 8 153 Modern Control System Learning by Brendon Smeresky and Alex Rizzo This book is immersed in the ubiquitous understanding of artificial intelligence with an overview of stochastic artificial intelligence followed by four chapters on such algorithms. A particularly important contribution prepares readers for the deter- ministic (non-stochastic) treatment of the topic: namely, deterministic algorithms can be used in stochastic artificial intelligence, but the approach remains stochastic. Deterministic artificial intelligence is examined next in three chapters that apply the approach to disparate facets of mechanical motion control. Deterministic artificial intelligence applied to motion control typically necessitates analytic expressions of desired motion displacement, velocity, and acceleration. The first two chapters of the section of this text examine methods of autonomous generation of such trajectories. The final chapter utilizes the prerequisite material to enumerate and critically evaluate the deterministic approach compared to nominal methods, including optimal and classical feedback methods. The text is meant for basic scientifically inclined readers who possess the basics of mathematics (while calculus certainly aids the reader to get more out of the chapters). The topics learned from reading this text will prepare students