Haibo He

Self-Adaptive Systems for Machine Intelligence (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 248
• Erscheinungstermin: 4. Mai 2011
• Englisch
• ISBN-13: 9781118025581
• Artikelnr.: 38232649

Autorenporträt

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.

Inhaltsangabe

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.