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Book synopsis

Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach examines the use of newly developed analytical tools for studying uncertainty analysis in engineering, control systems, and the sciences. It is the work of 38 experts who have written chapters on newly developed analytical methods - fuzzy logic, neural networks, simulation, and Bayesian techniques - and have applied them to uncertainty phenomena arising out of information and knowledge problems in the fields of engineering and the sciences. The book is divided into the following parts: Part I reports the theoretical studies on uncertainty types, models and measures; Part II reviews the applications of uncertain theoretical tools to engineering systems; Part III describes the methodologies of fuzzy-neural data analysis and forecasting; Part IV presents two chapters on fuzzy-neuro systems; and Part V describes the methodologies for fuzzy decision making and optimization and their computational methods. The Editors provide a concluding chapter on uncertainty and uncertainty modeling. This is a carefully developed book that treats the topic of uncertainty from fresh perspectives and in depth.

Book details

Book category Knihy po anglicky Computing & information technology Computer science Artificial intelligence

• Full title: Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approach
• Subtitle: Fuzzy Logic, Statistics, and Neural Network Approach
• Author: Bilal M. Ayyub, Madan M. Gupta
• Language: English
• Binding: Hardback
• Number of pages: 371
• EAN: 9780792380306
• ISBN: 0792380304
• ID: 01397389
• Publisher: Springer
• Weight: 706 g
• Dimensions: 161 × 242 × 27 mm
• Date of publishing: 31. October 1997

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