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.

Résumé
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.

Contenu

Philosophical and Theoretical Bases for Analyzing and Modeling Uncertainty and Ignorance.- A Self-Organizing Neural Network by Dynamic and Spatial Changing Weights.- Simulation of Fuzzy Systems I.- Simulation Of Fuzzy Systems II.- Event-Related Potential Noise Reduction Using the Hidden Markov Tree Model.- Change Detection in Image Sequence Based on Markov Random Field and Mean Field Theory.- Analysis of Multi-Channel Subdural EEG by Recurrent Neural Networks.- Multicriteria Optimization Under Parametric Uncertainty.- Design of Neural Networks for Pavement Rutting.- Neural Networks for Residential Infrastructure Management.- Evacuation Simulation in Underground Mall by Artificial Life Technology.- Epistemic Uncertainty and the Management of High Risk Exposures.- Experiment with a Hierarchical Text Categorization Method on WIPO Patent Collections.- Study of Transportation and Uncertainty.- Multi Agent Systems Approach to Parking Facilities Management.- Modeling Transportation Choice Through Utility-Based Multi-Layer Feedforward Networks.- Heterogeneity in Commuter Departure Time Decision: a Prospect Theoretic Approach.- Importance of Fuzzy Sets Definitions for Fuzzy Signal Controllers.- Reliability Evaluation of Realistic Structures Using FEM.- Simulation in Risk-Based Codified Engineering Design.- System Identification at Local Level under Uncertainty.- Uncertainty Modeling of Chloride Contamination and Corrosion of Concrete Bridges.- Redundancy Analysis of Structural Systems.