Details

Applied Research in Uncertainty Modeling and Analysis


Applied Research in Uncertainty Modeling and Analysis


International Series in Intelligent Technologies, Band 20

von: Bilal M. Ayyub

202,29 €

Verlag: Springer
Format: PDF
Veröffentl.: 29.12.2007
ISBN/EAN: 9780387235509
Sprache: englisch
Anzahl Seiten: 546

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Beschreibungen

The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
The application areas of uncertainty are numerous and diverse, including all fields of engineering, computer science, systems control and finance. Determining appropriate ways and methods of dealing with uncertainty has been a constant challenge. The theme for this book is better understanding and the application of uncertainty theories. This book, with invited chapters, deals with the uncertainty phenomena in diverse fields. The book is an outgrowth of the Fourth International Symposium on Uncertainty Modeling and Analysis (ISUMA), which was held at the center of Adult Education, College Park, Maryland, in September 2003. All of the chapters have been carefully edited, following a review process in which the editorial committee scrutinized each chapter. The contents of the book are reported in twenty-three chapters, covering more than . . ... pages. This book is divided into six main sections. Part I (Chapters 1-4) presents the philosophical and theoretical foundation of uncertainty, new computational directions in neural networks, and some theoretical foundation of fuzzy systems. Part I1 (Chapters 5-8) reports on biomedical and chemical engineering applications. The sections looks at noise reduction techniques using hidden Markov models, evaluation of biomedical signals using neural networks, and changes in medical image detection using Markov Random Field and Mean Field theory. One of the chapters reports on optimization in chemical engineering processes.
Self-Organizing Neural Networks by Dynamic and Spatial Changing Weights.- Uncertainty in the Automation of Ontology Matching.- Uncertainty Modeling of Data and Uncertainty Propagation for Risk Studies.- Development of Quadratic Neural Unit with Applications to Pattern Classification.- Quadratic and Cubic Neural Units for Identification and Fast State Feedback Control of Unknown Non-Linear Dynamic Systems.- Crisp Simulation of Fuzzy Computations.- Exploratory Modeling Managing Uncertain Risk.- Multi-Interval Elicitation of Random Intervals for Engineering Reliability Analysis.- Biological Applications.- Engineering and Sciences.- Transportation Engineering.- Structural Engineering.
Uncertainty has been a concern to engineers, managers, and scientists for many years. For a long time uncertainty has been considered synonymous with random, stochastic, statistic, or probabilistic. Since the early sixties views on uncertainty have become more heterogeneous.  In the past forty years numerous tools that model uncertainty, above and beyond statistics, have been proposed by several engineers and scientists. The tool/method to model uncertainty in a specific context should really be chosen by considering the features of the phenomenon under consideration, not independent of what is known about the system and what causes uncertainty.
In this fascinating overview of the field, the authors provide broad coverage of uncertainty analysis/modeling and its application. Applied Research in Uncertainty Modeling and Analysis presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise. Rather than focusing explicitly on theory, the authors use real-world examples to demonstrate the strength of the chosen methodology.
Applied Research in Uncertainty Modeling and Analysis concentrates on general aspects of uncertainty, modeling, and methods, and focuses on various applications, included Biomedical Engineering, Chemical Engineering, Structural Engineering, and Transportation Engineering.
Provides broad coverage of uncertainty analysis/modeling and its application
Presents the perspectives of various researchers and practitioners on uncertainty analysis and modeling outside their own fields and domain expertise
Focusing explicitly on theory, this work uses real-world examples to demonstrate the strength of the chosen methodology

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