Badru A.D. Artificial Intelligence and Digital Systems Engineering

Boca Raton: CRC Press, 2022. — 128 p.The resurgence of Artificial Intelligence has been fueled by the availability of the present generation of high-performance computational tools and techniques. This book is designed to provide introductory guidance to Artificial Intelligence (AI), particularly from the perspective of digital systems engineering.Artificial Intelligence and Digital Systems Engineering provides a general introduction to the origin of AI and covers the wide application areas and software and hardware interfaces. It will prove to be instrumental in helping new users expand their knowledge horizon to the growing market of AI tools, as well as showing how AI is applicable to the development of games, simulation, and consumer products, particularly using artificial neural networks.Artificial Intelligence (AI) is not just one single thing. It is a conglomerate of various elements, involving software, hardware, data platform, policy, procedures, specifications, rules, and people intuition. How we leverage such a multifaceted system to do seemingly intelligent things, typical of how humans think and work, is a matter of systems implementation. This is why the premise of this book centers on a systems methodology. In spite of the recent boost in the visibility and hype of artificial intelligence, it has actually been around and toyed with for decades. What has brought AI more to the forefront nowadays is the availability and prevalence of high-powered computing tools that have enabled the data-intensive processing required by AI systems. The resurgence of AI has been driven by the following developments:The emergence of new computational techniques and more powerful computers.
Machine Learning techniques.
Autonomous systems.
New/innovative applications.
Specialized techniques: Intelligent Computational Search Technique Using Cantor Set Sectioning.
Human-in-the-loop requirements.
Systems integration aspects.
This book is for the general reader, university students, and instructors of industrial, production, civil, mechanical, and manufacturing engineering. It will also be of interest to managers of technology, projects, business, plants, and operations.Understanding AI.
Expert Systems: The Software Side of AI.
Digital Systems Framework for AI.
Neural Networks for Artificial Intelligence.Definition of a Neurode.
Variations of a Neurode.
Single Neurode: The McCullough-Pitts Neurode.
Single Neurode as Binary Classifier.
Single Neurode Perceptron.
Associative Memory.
Correlation Matrix Memory.
Widrow – Hoff Approach.
LMS Approach.
Adaptive Correlation Matrix Memory.
Error-Correcting Pseudo-Inverse Method.
Self-Organizing Networks.
Principal Components.
Clustering by Hebbian Learning.
Clustering by Oja’s Normalization.
Competitive Learning Network.
Multiple-Layer Feedforward Network.
Radial Basis Networks.
Interpolation.
Radial Basis Network.
Single-Layer Feedback Network.
Discrete Single-Layer Feedback Network.
Bidirectional Associative Memory.
Hopfield Network.Neural-Fuzzy Networks for Artificial Intelligence.