- Hardcover: 400 pages
- Publisher: Chapman and Hall/CRC; 1 edition (May 25, 2006)
- Language: English
- ISBN-10: 1584886447
- ISBN-13: 978-1584886440
- Product Dimensions: 6 x 0.8 x 9.2 inches
- Shipping Weight: 1.6 pounds (View shipping rates and policies)
- Average Customer Review: 11 customer reviews
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Uncertainty Modeling and Analysis in Engineering and the Sciences 1st Edition
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The book is written to assist analyst and designers in understanding the fundamentals of knowledge and ignorance, how to model and analyze uncertainty, and how to select appropriate analytical tools for a particular problem. The authors adopt a more generalized approach to uncertainty analysis that includes both probabilistic and nonprobabilistic methods.
The book begins with this quote from Stephen Hawking: "The greatest enemy of knowledge is not ignorance; it is the illusion of knowledge". This quote was deliberately chosen by the authors to set the tone for the discussion on the close connection between uncertainty, information, ignorance and knowledge. The authors look at the meaning, nature, and hierarchy of knowledge and ignorance from a philosophical perspective; where the close connection between uncertainty and information as a pair, and ignorance and knowledge as another pair are carefully examined. The subject of Systems Framework is well treated, with emphasis on taxonomy of systems, complexity of systems, and systems knowledge.
In order to quantify and analyze uncertainty, the data that contains or expresses the uncertainty in a system should be organized in a structured manner so that it can lend itself to mathematical or logical representation and analysis. This leads the authors to explore methods of encoding data. The concept of Identification and Classification of Theories is used as a basis for defining a universal set based on closed-world and open-world assumptions, from which elements of the universal set (precise or imprecise), and sets (also precise or imprecise) are developed. The fundamentals of classical set theory, probability theory, fuzzy sets, generalized measures, rough sets, and gray systems are presented as a formalized way of encoding data. Numerical examples are used to explain the basic mathematical operations of fuzzy sets and rough sets. However, not much attention is paid to gray sets.
In order to make appropriate decisions about a system, engineers and scientists need to synthesis data and information. This leads the authors to the subject of uncertainty and information synthesis. Here, measure theory and monotone measures, including possibility theory and the Dempster-Shafer theory of evidence are explained. They then compare and contrast these theories with some variations of probability theory, such as Bayesian probabilities, and interval probabilities. Simple numerical problems are used to demonstrate how these methods can be used to aggregate expert opinion.
The concept of aggregating expert opinion is further examined with demonstrative examples by utilizing minimum and maximum uncertainty criterion, and uncertainty invariance criterion. The book also presents methods for propagating uncertainty in input-output systems. The processes of eliciting expert opinions also receive a fair amount of coverage in the book. The authors demonstrate the applications of expert opinion elicitation with practical examples. Techniques for visualizing uncertainty in information are also presented.
Uncertainty Modeling and Analysis in Engineering and the Sciences presents a holistic view of understanding uncertainty in systems that are of relevance to engineers and scientists in practice, in a more generalized approach, and is a useful book that provides a fundamental understanding to analyzing uncertainty in engineering and scientific systems. The authors demonstrate a deep understanding of the subject matter and draw on real word examples to explain the concepts presented in the book.
I have worked for many years in designing high reliable notebook computers in an international company. Uncertainties have caused many persisting problems to my research and develop work. One simple case is the free drop of a notebook computer. No matter how hard I tried to control the drop, there was no useful pattern of shocks which were generated by the drops. Moreover, I cannot drop hundred of these kind of expensive products to get adequate data, thus it seemed almost impossible to make rational decisions in treating drops. I am lucky that I got the chance to attend the author Professor B. M. Ayyub's class and find that this book is quite useful to deal with my problem. Professor Ayyub is a leading authority in the areas of risk analysis, uncertainty modeling, decision analysis, and systems engineering in the world. Another author, George J. Klir is a distinguished professor of systems science at Binghamton University, State University of New York. Their experience and success in academy and industry guaranteed the quality of this book.
At the beginning of the book, the author presented different philosophies and related topics about uncertainty. No technique tools are provided, but the presented information is quite enlightening for engineering and scientific work. The reader is also encouraged to cultivate a habit to analyze problems in a systematic manner, which is quite important but is often ignored. Examples given for this purpose in the book may look overly concise, but because of the difficulty to provide so much information from different schools of philosophy in one chapter, the author has to cut off some information. The first chapter can serve as an independent material for reading. Even if you don't have time to finish the whole book, do not miss the first chapter.
When the reader has the basic understanding of system and uncertainty, the author begins to help the reader to recollect the classical theory of probability, and then establishes a passage from the classical theory to the uncertainty theory. It is worth noticing that, on one hand, the languages, techniques and examples chosen are simple enough for most readers. Even if you have trouble to remember the classical probability theory of your undergraduate study, you won't feel very hard to catch up with the book. On the other hand, this simplification doesn't hurt the information that should be conveyed to the reader. When the passage is established, the reader can find out that she or he can benefit in two aspects: for one thing, the reader can understand some important principles of uncertainty theory more easily based on her or his previous understanding of the counterpart classical theory principles. For another, many methods in dealing with uncertainties can be converted into problems in classical theory, which has plentiful of matured tools. In the following chapters, the reader learned useful skills like synthesizing uncertainty and information, measuring uncertainty, and finally learned how to model uncertainty and analyze it for specific engineering and scientific problems.
Although this book is aimed at engineering and sciences, professionals and students in other areas like marketing and management can also benefit from this book. For example, in computer industry, product managers usually finds it difficult to make a proper product specification to address a new or emerging market without some of the critical information. This book can help them to analyze the uncertainty and to be more able to make the right decision. For the people who want to dive deeper into this area, this book provides a valuable list of reference.
Every book causes some kind of inconvenience for its readers. In this book, the exercise problems at the end of each chapter are well designed, but there is no answer to the question. The book in general is quite well organized, but there is a couple of terms are not inconsistently used. However, I think they won't cause too much trouble for most readers.
In sum, this book provides effective medicine to treat uncertainty, the pain point for most engineering and scientific problems. People who have studied undergraduate probability theory won't have any trouble in reading it. Except textbook, this book can also serve as a valuable manual and handbook for data organizing and decision making in research.