Uncertainty Analysis in Engineering and Sciences ― Fuzzy Logic, Statistics, and Neural Network Approach

Examines the use of newly developed analytical tools such as fuzzy logic, neural networks, simulation, and Bayesian techniques for studying uncertainty analysis in engineering, control systems, and the sciences. Part I reports on theoretical studies of uncertainty types, models, and measures, and Part II reviews applications of theoretical tools to engineering systems. Parts III and IV describe methodologies of fuzzy-neural data analysis and forecasting, and discuss fuzzy- neuro systems. Part V describes methodologies for fuzzy decision making and optimization and their computational methods. A concluding chapter explores uncertainty modeling. Based on papers from a September 1995 conference. Annotation c. by Book News, Inc., Portland, Or.