This book will advance the understanding and application ofself-adaptive intelligent systems; therefore it will potentiallybenefit the long-term goal of replicating certain levels ofbrain-like intelligence in complex and networked engineeringsystems. It will provide new approaches for adaptive systems withinuncertain environments. This will provide an opportunity toevaluate the strengths and weaknesses of the currentstate-of-the-art of knowledge, give rise to new researchdirections, and educate future professionals in this domain.
Self-adaptive intelligent systems have wide applications frommilitary security systems to civilian daily life. In this book,different application problems, including pattern recognition,classification, image recovery, and sequence learning, will bepresented to show the capability of the proposed systems inlearning, memory, and prediction. Therefore, this book will alsoprovide potential new solutions to many real-worldapplications.
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About the Author
Haibo He, PhD, is Assistant Professor in the Department ofElectrical, Computer, and Biomedical Engineering at the Universityof Rhode Island. His primary research interest is computationalintelligence and self-adaptive systems, including optimization andprediction, biologically inspired machine intelligence, machinelearning and data mining, hardware design (VLSI/FPGA) for machineintelligence, as well as various application fields such as smartgrid, sensor networks, and cognitive radio networks.
Table of Contents
Chapter 1. Introduction.
1.1 The Machine Intelligence Research.
1.2 The Two-Fold Objectives: Data-Driven andBiologically-Inspired Approaches.
1.3 How to Read this Book.
1.4 Summary and Further Reading.
Chapter 2. Incremental Learning.
2.2 Problem Foundation.
2.3 An Adaptive Incremental Learning Framework.
2.4 Design of the Mapping Function.
2.5 Case Study.
Chapter 3. Imbalanced Learning.
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.
Chapter 4. Ensemble Learning.
4.2 Hypothesis Diversity.
4.3 Developing Multiple Hypotheses.
4.4 Integrating Multiple Hypotheses.
4.5 Case Study.
Chapter 5. Adaptive Dynamic Programming for MachineIntelligence.
5.2 Fundamental Objectives: Optimization and Prediction.
5.3 ADP for Machine Intelligence.
5.4 Case Study.
Chapter 6. Associative Learning.
6.2 Associative Learning Mechanism.
6.3 Associative Learning in Hierarchical Neural Networks.
6.4 Case Study.
Chapter 7. Sequence Learning.
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 andRetrieval.
7.6 Memory Requirement.
7.7 Learning and Anticipation of Multiple Sequences.
7.8 Case Study.
Chapter 8. Hardware Design for Machine Intelligence.
8.1 A Final Comment.
What People are Saying About This
"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)