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.

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.