Название: Artificial Intelligence and Digital Systems Engineering Автор: Adedeji B. Badiru Издательство: CRC Press Год: 2022 Страниц: 128 Язык: английский Формат: pdf, epub Размер: 10.1 MB
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:
• 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.
Preface Acknowledgments About the Author 1 Understanding AI 2 Expert Systems: The Software Side of AI 3 Digital Systems Framework for AI 4 Neural Networks for Artificial Intelligence Introduction 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 Summary References 5 Neural-Fuzzy Networks for Artificial Intelligence Index
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