Badru A.D. Artificial Intelligence and Digital Systems Engineering

Boca Raton: CRC Press, 2022. — 128 p.he 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:
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.About the Author
Understanding AI
Expert Systems: The Software Side of AI
Digital Systems Framework for AI
Neural Networks for Artificial IntelligenceDefinition 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 NetworkNeural-Fuzzy Networks for Artificial Intelligence