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Applied Research in Uncertainty Modeling and Analysis [Paperback]

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  • Category: Books (Business & Economics)
  • ISBN-10:  1441936351
  • ISBN-10:  1441936351
  • ISBN-13:  9781441936356
  • ISBN-13:  9781441936356
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  545
  • Pages:  545
  • Binding:  Paperback
  • Binding:  Paperback
  • Pub Date:  01-Feb-2010
  • Pub Date:  01-Feb-2010
  • SKU:  1441936351-11-SPRI
  • SKU:  1441936351-11-SPRI
  • Item ID: 100719902
  • List Price: $169.99
  • Seller: ShopSpell
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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.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 Uncertailó:
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