Open access peer-reviewed Edited Volume

Deterministic Artificial Intelligence

Edited by Timothy Sands

Stanford University; Columbia University; Naval Postgraduate School

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 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 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, 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 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.

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Deterministic Artificial IntelligenceEdited by Timothy Sands

Published: May 27th 2020

DOI: 10.5772/intechopen.81309

ISBN: 978-1-78984-112-1

Print ISBN: 978-1-78984-111-4

eBook (PDF) ISBN: 978-1-83880-728-3

Copyright year: 2020

Books open for chapter submissions

220 Total Chapter Downloads

chaptersDownloads

Open access peer-reviewed

1. Stochastic Artificial Intelligence: Review Article

By T.D. Raheni and P. Thirumoorthi

39

Open access peer-reviewed

2. 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

27

Open access peer-reviewed

3. 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

28

Open access peer-reviewed

4. Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

By Behzad Vaferi

35

Open access peer-reviewed

5. The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements

By Tatyana Zubkova and Marina Tokareva

28

Open access peer-reviewed

6. Deterministic Approaches to Transient Trajectory Generation

By Matthew A. Cooper

17

Open access peer-reviewed

7. Sinusoidal Trajectory Generation Methods for Spacecraft Feedforward Control

By Kyle A. Baker

25

Open access peer-reviewed

8. Modern Control System Learning

By Brendon Smeresky and Alex Rizzo

22

Edited Volume and chapters are indexed in

  • Worldcat
  • OpenAIRE
  • Google Scholar
  • AZ ebsco
  • Base
  • CNKI

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