Deterministic Artificial Intelligence
eBook - PDF

Deterministic Artificial Intelligence

  1. 178 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Deterministic Artificial Intelligence

About this book

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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Yes, you can access Deterministic Artificial Intelligence by Timothy Sands in PDF and/or ePUB format, as well as other popular books in Computer Science & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Deterministic Artificial Intelligence
  2. Contents
  3. Preface
  4. Section 1 - Stochastic Approaches
  5. Chapter 1 - Stochastic Artificial Intelligence: Review Article
  6. Chapter 2 - Simulated Real-Time Controller for Tuning Algorithm Using Modified Hill Climbing Approach Based on Model Reference Adaptive Control System
  7. Chapter 3 - Random Forest-Based Ensemble Machine Learning Data-Optimization Approach for Smart Grid Impedance Predictionin the Powerline Narrowband Frequency Band
  8. Chapter 4 - Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids
  9. Chapter 5 - The Technique of Automated Design of Technological Objects with the Application of Artificial Intelligence Elements
  10. Section 2 - Deterministic Approaches
  11. Chapter 6 - Deterministic Approaches to Transient Trajectory Generation
  12. Chapter 7 - Sinusoidal Trajectory Generation Methods for Spacecraft Feedforward Control
  13. Chapter 8 - Modern Control System Learning