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Informationen zum Autor Earl founded and serves as President of, Scianta Intelligence, a next generation machine intelligence and knowledge exploration company. He is a futurist, author, management consultant, and educator involved in discovering the epistemology of advanced intelligent systems, the redefinition of the machine mind, and, as a pioneer of Internet-based technologies, the way in which evolving inter-connected virtual worlds will affect the sociology of business and culture in the near and far future. Earl has over thirty years experience in managing and participating in the software development process at the system as well as tightly integrated application level. In the area of advanced machine intelligence technologies, Earl is a recognized expert in fuzzy logic, and adaptive fuzzy systems as they are applied to information and decision theory. He has pioneered the integration of fuzzy neural systems with genetic algorithms and case-based reasoning. As an industry observer and futurist, Earl has written and talked extensively on the philosophy of the Response to Change, the nature of Emergent Intelligence, and the Meaning of Information Entropy in Mind and Machine. Zusammenfassung Suitable for analysts, engineers and managers involved in developing data mining models in business and government, this work helps you to understand the trade-offs implicit in various models and model architectures. It provides coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.
Sommario
Preface
Acknowledgements
Introduction
PART ONE - CONCEPTS AND ISSUES
Chapter 1. Foundations and Ideas
Chapter 2. Principal Model Types
Chapter 3. Approaches to Model Building
PART TWO - FUZZY SYSTEMS
Chapter 4. Fundamental Concepts of Fuzzy Logic
Chapter 5. Fundamental Concepts of Fuzzy Systems
Chapter 6. FuzzySQL and Intelligent Queries
Chapter 7. Fuzzy Clustering
Chapter 8. Fuzzy Rule Induction
PART THREE - EVOLUTIONARY STRATEGIES
Chapter 9. Fundamental Concepts of Genetic Algorithms
Chapter 10. Genetic Resource Scheduling Optimization
Chapter 11. Genetic Tuning of Fuzzy Models
