Knowledge Processing with Interval and Soft Computing

ISBN-13: 9781848003255 / Angielski / Twarda / 2008 / 233 str.

Chenyi Hu; R. Baker Kearfott; Andre De Korvin

Opis

Interval computing combined with fuzzy logic has become an emerging tool in studying artificial intelligence and knowledge processing (AIKP) applications since it models uncertainties frequently raised in the field. This book provides introductions for both interval and fuzzy computing in a very accessible style. Application algorithms covered in this book include quantitative and qualitative data mining with interval valued datasets, decision making systems with interval valued parameters, interval valued Nash games and interval weighted graphs. Successful applications in studying finance and economics, etc are also included. This book can serve as a handbook or a text for readers interested in applying interval and soft computing for AIKP.

Spis treści

Fundamentals of Interval Computing.- Soft Computing Essentials.- Relations Between Interval Computing and Soft Computing.- Interval Matrices in Knowledge Discovery.- Interval Function Approximation and Applications.- Interval Rule Matrices for Decision Making.- Interval Matrix Games.- Interval-Weighted Graphs and Flow Networks.- Arithmetic on Bounded Families of Distributions A Denv Algorithm Tutorial.- IntBox An Object-Oriented Interval Computing Software Toolbox in C#x002B;#x002B;.

Blurb

Massive datasets, made available today by modern technologies, present a significant challenge to scientists who need to effectively and efficiently extract relevant knowledge and information.

Due to their ability to model uncertainty, interval and soft computing techniques have been found to be effective in this extraction. This book provides coverage of the basic theoretical foundations for applying these techniques to artificial intelligence and knowledge processing.

The first three chapters provide the background needed for those who are unfamiliar with interval and soft computing techniques. The following chapters describe innovative algorithms and their applications to knowledge processing.

In particular, these chapters cover computing techniques for interval linear systems of equations, interval matrix singular-value decomposition, interval function approximation, and decision making with statistical and graph-based data processing. To enable these applications, the book presents a standards-based object-oriented interval computing environment in C++.

By providing the necessary background and summarizing recent results and successful applications, this self-contained book will serve as a useful resource for researchers and practitioners wanting to learn interval and soft computing techniques and apply them to artificial intelligence and knowledge processing.