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Title: Intelligent Computing: A Computing Approach to Artificial Intelligence, Edition 1.5
Author: Hon Keung Kwan
eBook: 721 pages
Language: English
Publisher: dfisp.org
Publication Date: February 4, 2020
ISBN13: 9781988307091
Dimensions: 6 x 9 inches
Format: Adobe Digital Editions
Platform: Windows, Machintosh, and Andriod desktop/ laptop/ tablet/ phone
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Reviews
Dr. Chi-Sang Poon, Principal Research Scientist, Medical Engineering and Sciences, Massachusetts Institute of Technology, USA [14 November, 2017]:
The eBook is written in a very readable and easy to understand manner to explain intelligent computing and system design covering neural computing, fuzzy neural computing, and evolutionary computing which can be used to solve complex practical problems across multidisciplinary fields. The many illustrative examples provided throughout the eBook are invaluable for the purpose of self-study. It is highly recommended as an academic textbook and a professional reference for the subject area of intelligent computing (or computational intelligence).
Dr. Liang Chen, Professor of Computer Science, University of Northern British Columbia, Canada [14 November, 2017]:
The eBook gives a detailed but clear description, supported by many step-by-step examples and practical design examples, to explain the three core areas of intelligent computing and system design. This eBook is an outstanding choice as a senior undergraduate and graduate textbook for learning and understanding the subject. As a computing professional, this eBook is a must-have handbook for applying soft computing techniques to practical problems.
Dr. Felix Albu, Professor of Electronics, Telecommunications and Electrical Engineering, Valahia University of Targoviste, Romania [18 November 2017]:
This exceptional eBook provides a great coverage on the theories, models, algorithms, and design methodologies of intelligent computing and system design. It includes plenty of figures, examples, and design examples explaining every concept and its implementation in a creative and intuitive way. I really like this eBook because it shows essential elements of quality teaching for effective learning in the presented areas, and I highly recommend it to senior undergraduate and graduate students, and professionals interested in learning various aspects of computational intelligence.
Testimonials
Helen Mulugeta, B.App.Sc.(2017-2021), Department of Electrical and Computer Engineering, University of Windsor, Canada [24 May 2021]:
This eBook and its MATLAB and Worked Problems eBooks introduce intelligent computing as a computing approach to artificial intelligence. Concepts, methodologies, and algorithms in neural computing, fuzzy neural computing, evolutionary computing, and deep neural computing are clearly described and explained. Simple and practical step-by-step hand-calculation examples and problems as well as their MATLAB programs are used in these eBooks. I highly recommend this comprehensive set of eBooks to anyone interested to learn and master this rapidly growing intelligent computing field.
Chris Nardone, B.App.Sc.(2017-2021), Department of Electrical and Computer Engineering, University of Windsor, Canada [14 May 2021]:
This eBook on Intelligent Computing adopts a simple and clear approach to explain the theories, models, algorithms, and design methodologies of neural, fuzzy neural, evolutionary, and deep neural computing. This eBook and its accompanying MATLAB and Worked Problems eBook contains many illustrative figures, self-explanatory tables, and easy-to-follow examples and MATLAB programs to reinforce learning. I highly recommend this set of eBooks as they offer an excellent guide to Intelligent Computing for anyone wishing to learn the knowledge and master the techniques in this important field.
Salman Khan Pathan, M.Eng.(2020-2021), Department of Electrical and Computer Engineering, University of Windsor, Canada [13 May 2021]:
This eBook is clear and concise and offers much more information than I expected. This eBook and its supplementary MATLAB and worked problems eBook enable me to pass my course with a good grade. The eBook provides a comprehensive coverage of intelligent computing including all the aspects of computational intelligence and is readable and easy to understand. I recommend this eBook to students and professionals who would like to excel in this subject.
Siyu Zhang, M.App.Sc.(2014-2016), Department of Electrical and Computer Engineering, University of Windsor, Canada [22 January 2018]:
This eBook on intelligent computing includes all the key chapters in neural computing, fuzzy computing, and evolutionary computing. The contents of the eBook are explained in step-by-step which are easy to understand. Many clear and color figures are used for illustration, many self-explanatory tables are adopted to summarize important facts, and many numerical examples are given to explain concepts. The main eBook and the accompanying Matlab and worked problems eBook are definitely required for students to do well in the author’s courses, and are top choices for engineers and researchers to self-learn the subject.
Rija Raju, Ph.D.(2015-2020), Algorithm Developer, Ericsson Canada [28 December 2017]:
This eBook illustrates the basics and advanced topics in intelligent computing including neural computing, fuzzy neural computing, and evolutionary computing in a lucid and coherent manner. The concepts are well explained using step by step mathematical calculations and implemented through Matlab examples, thereby making this book a one-stop shop for all intelligent computing and system design. I have utilized this eBook to fathom the principles of computational intelligence including evolutionary algorithms, which has helped me to advance my research in the field of artificial intelligence. As a PhD candidate, I believe this eBook is an excellent choice not only to students and researchers, but also to the information thirsty readers in the domain of machine intelligence.
Mayokun Ajiboye, M.Eng.(2013-2014), M.App.Sc.(2014-2015), Department of Electrical and Computer Engineering, University of Windsor, Canada [16 December 2017]:
The author has done a great work explaining the nitty-gritty of intelligent computing including the evolutionary algorithms, supported by design examples which can be tried out to confirm your understanding of the concepts and applications. The eBook has been an invaluable guide for my graduate studies as it tackles practical problems without superfluous of principles. I believe you’ll enjoy exploring the contents of the eBook as much as I do.
Hardanish Singh, M.Eng.(2016-2017), Department of Electrical and Computer Engineering, University of Windsor, Canada [1 December 2017]:
The eBook is written in a manner which is easy to understand and contains well-designed examples for students to learn the topics effectively. This eBook is a required textbook for the author’s 06-88533 course on Computational Intelligence taken by me in the winter 2017 term as a Master of Engineering Student (2016-2017) in which I secured a grade of A+. Based on my personal experience, I would strongly recommend this eBook to all students.
Editorial Review
1. Description
The Edition 1.5 of this eBook on Intelligent Computing gives a clear description of the theories, models, algorithms, and design methodologies of intelligent computing to mimic and explain human-like reasoning, learning, and memory.
The eBook consists of 20 chapters organized into 5 parts.
The Part I on Intelligent Computing consists of Chapter 1 which gives overviews on computational intelligence, artificial intelligence, intelligent computing, and intelligent system design; and the basics of biological and artificial neural networks.
The Part II on Neural Computing starts with an overview and consists of Chapters 2 to 8 covering various neural networks and learning algorithms.
The Part III on Fuzzy Neural Computing first provides an overview and consists of Chapters 9 to 12 explaining fuzzy set, fuzzy logic and reasoning, fuzzy systems; and fuzzy neural networks.
The Part IV on Evolutionary Computing begins with an overview and consists of Chapter 13 to 17 describing 4 popular evolutionary algorithms, and aspects on coding, initialization, and constraints.
Finally, the Part V is on Deep Neural Computing first presents an overview and consists of Chapters 18-20 on multiplierless neural networks; convolutional neural networks; and deep residual learning and networks.
2. Key Features
A comprehensive coverage of intelligent computing.
Topics are clearly described and explained with the aid of step-by-step hand-calculation examples, Matlab and design examples, figures, tables, and easy-to-understand equations.
The eBook contains 98 examples in which 37 of them are design examples; and over 150 tables, 176 figures, 195 references, and a total of 721 pages to support and clarify explanations.
Accompanied by a supplementary eBook on “Matlab and Worked Problems in Intelligent Computing” to reinforce learning.
A clickable table of contents also comes with the eBook to allow a quick link of a specific topic.
The eBook has been designed and formatted to be readable and usable directly by an instructor (and teaching assistants) in regular and online lecture (and tutorial) classes without a need to prepare presentation slides.
The contents of this eBook have been developed under research and classroom environments for courses taught by the author.
3. Readership
Senior undergraduate and graduate students in engineering, computer science, informatics, management science, and other programs taking a course in intelligent Computing: A Computing Approach to Artificial Intelligence.
This eBook contains core and advanced topics to meet the curriculum requirements of both undergraduate and graduate courses.
Practising engineers, computing professionals, and anyone who are interested to self-learn, understand, and/or explore new possibilities for research and project in neural networks, fuzzy neural systems, evolutionary computing, and deep learning and neural networks for building intelligent systems.
No specific prerequisites are required but a background of elementary courses in linear algebra and calculus would be useful.
4. About the Author
The author received his D.I.C. and Ph.D. degree in Electrical Engineering (Signal Processing) in 1981 from the Imperial College London, United Kingdom. He has served since 1989 as a Professor of Electrical and Computer Engineering at the University of Windsor where he initiated and started the development of computational intelligence research program and courses. Prior to this, he had served as a faculty member for seven years at the University of Hong Kong where he initiated and started the development of digital signal processing research program and courses since 1981. He was elected in 1996 a Fellow of the Institution of Engineering and Technology. He has written eight eBooks (published by dfisp.org) and five book chapters, co-edited two printed books, and published over 300 journal and conference papers in digital signal processing and computational intelligence. Dr. Kwan had served as the Chair (2003-2004) of the Neural Systems and Applications Technical Committee and the Chair (2004-2006) of Digital Signal Processing Technical Committee of the IEEE Circuits and Systems Society, and the Chair (2008-2009) of Intelligent Systems and Applications Technical Committee of Asia Pacific Signal and Information Processing Association. He is a registered P.Eng. in Ontario, Canada.
Part I Intelligent Computing
Chapter 1 Introduction to Intelligent Computing
Part II Neural Computing
Chapter 2 One-Layer Feedforward Neural Networks
Chapter 3 One-Layer Recurrent Neural Networks
Chapter 4 Multilayer Neural Networks
Chapter 5 Associative Memories
Chapter 6 Self-Organizing Map
Chapter 7 Learning Vector Quantization
Chapter 8 Radial Basis Function Networks
Part III Fuzzy Neural Computing
Chapter 9 Fuzzy Sets
Chapter 10 Fuzzy Logic and Reasoning
Chapter 11 Fuzzy Systems
Chapter 12 Fuzzy Neural Networks
Part IV Evolutionary Computing
Chapter 13 Genetic Algorithms
Chapter 14 Differential Evolution
Chapter 15 Particle Swarm Optimization
Chapter 16 Simulated Annealing
Chapter 17 Coding, Initialization, and Constraints
Part V Deep Neural Computing
Chapter 18 Multiplierless Neural Networks
Chapter 19 Convolutional Neural Networks
Chapter 20 Deep Residual Learning and Networks
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