Self-Adaptive Systems for Machine Intelligence
Published on 15. July 2011
Book
Hardback
248 pages
978-0-470-34396-8 (ISBN)
Description
This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain.
Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
Reviews / Votes
"This comprehensive introduction to machine intelligence engineering and self-adaptive systems provides an overview of a variety of processes and technologies for the development of artificial intelligence." (Book News, 1 October 2011)More details
Product info
gebunden
Edition
1. Auflage
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Paper over boards
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 18 mm
Weight
542 gr
ISBN-13
978-0-470-34396-8 (9780470343968)
Schweitzer Classification
Other editions
Additional editions
E-Book
09/2011
Wiley
€89.99
Available for download
E-Book
05/2011
Wiley
€89.99
Available for download
Person
Haibo He, PhD, is Assistant Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island. His primary research interest is computational intelligence and self-adaptive systems, including optimization and prediction, biologically inspired machine intelligence, machine learning and data mining, hardware design (VLSI/FPGA) for machine intelligence, as well as various application fields such as smart grid, sensor networks, and cognitive radio networks.
Content
Preface.
Acknowledgments.
Chapter 1. Introduction.
1.1 The Machine Intelligence Research.
1.2 The Two-Fold Objectives: Data-Driven and Biologically-Inspired Approaches.
1.3 How to Read this Book.
1.4 Summary and Further Reading.
References.
Chapter 2. Incremental Learning.
2.1 Introduction.
2.2 Problem Foundation.
2.3 An Adaptive Incremental Learning Framework.
2.4 Design of the Mapping Function.
2.5 Case Study.
2.6 Summary.
Chapter 3. Imbalanced Learning.
3.1 Introduction.
3.2 Nature of the Imbalanced Learning.
3.3 Solutions for Imbalanced Learning.
3.4 Assessment Metrics for Imbalanced Learning.
3.5 Opportunities and Challenges.
3.6 Case Study.
3.7 Summary.
Chapter 4. Ensemble Learning.
4.1 Introduction.
4.2 Hypothesis Diversity.
4.3 Developing Multiple Hypotheses.
4.4 Integrating Multiple Hypotheses.
4.5 Case Study.
4.6 Summary.
Chapter 5. Adaptive Dynamic Programming for Machine Intelligence.
5.1 Introduction.
5.2 Fundamental Objectives: Optimization and Prediction.
5.3 ADP for Machine Intelligence.
5.4 Case Study.
5.5 Summary.
Chapter 6. Associative Learning.
6.1 Introduction.
6.2 Associative Learning Mechanism.
6.3 Associative Learning in Hierarchical Neural Networks.
6.4 Case Study.
6.5 Summary.
Chapter 7. Sequence Learning.
7.1 Introduction.
7.2 Foundations for Sequence Learning.
7.3 Sequence Learning in Hierarchical Neural Structure.
7.4 Level 0: A Modified Hebbian Learning Architecture.
7.5 Level 1 to Level N: Sequence Storage, Prediction and Retrieval.
7.6 Memory Requirement.
7.7 Learning and Anticipation of Multiple Sequences.
7.8 Case Study.
7.9 Summary.
Chapter 8. Hardware Design for Machine Intelligence.
8.1 A Final Comment.
References.