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Author: Cornelius T. Leondes
Publisher: Elsevier
ISBN: 0080553192
Category : Computers
Languages : en
Pages : 437
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Book Description
This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. - Fuzzy system techniques applied to neural networks for modeling and control - Systematic design procedures for realizing fuzzy neural systems - Techniques for the design of rule-based expert systems - Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets - System identification and control - Nonparametric, nonlinear estimation Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies
Author: Cornelius T. Leondes
Publisher: Elsevier
ISBN: 0080553192
Category : Computers
Languages : en
Pages : 437
Get Book Here
Book Description
This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies. - Fuzzy system techniques applied to neural networks for modeling and control - Systematic design procedures for realizing fuzzy neural systems - Techniques for the design of rule-based expert systems - Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets - System identification and control - Nonparametric, nonlinear estimation Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies
Author: Kate A. Smith
Publisher: IGI Global
ISBN: 9781931777797
Category : Computers
Languages : en
Pages : 274
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Book Description
"For professionals, students, and academics interested in applying neural networks to a variety of business applications, this reference book introduces the three most common neural network models and how they work. A wide range of business applications and a series of global case studies are presented to illustrate the neural network models provided. Each model or technique is discussed in detail and used to solve a business problem such as managing direct marketing, calculating foreign exchange rates, and improving cash flow forecasting."
Author: Xingui He
Publisher: Springer Science & Business Media
ISBN: 3540737626
Category : Computers
Languages : en
Pages : 240
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Book Description
For the first time, this book sets forth the concept and model for a process neural network. You’ll discover how a process neural network expands the mapping relationship between the input and output of traditional neural networks and greatly enhances the expression capability of artificial neural networks. Detailed illustrations help you visualize information processing flow and the mapping relationship between inputs and outputs.
Author:
Publisher: Academic Press
ISBN: 0080553907
Category : Computers
Languages : en
Pages : 459
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Book Description
The book emphasizes neural network structures for achieving practical and effective systems, and provides many examples. Practitioners, researchers, and students in industrial, manufacturing, electrical, mechanical,and production engineering will find this volume a unique and comprehensive reference source for diverse application methodologies. Control and Dynamic Systems covers the important topics of highly effective Orthogonal Activation Function Based Neural Network System Architecture, multi-layer recurrent neural networks for synthesizing and implementing real-time linear control,adaptive control of unknown nonlinear dynamical systems, Optimal Tracking Neural Controller techniques, a consideration of unified approximation theory and applications, techniques for the determination of multi-variable nonlinear model structures for dynamic systems with a detailed treatment of relevant system model input determination, High Order Neural Networks and Recurrent High Order Neural Networks, High Order Moment Neural Array Systems, Online Learning Neural Network controllers, and Radial Bias Function techniques. Coverage includes: - Orthogonal Activation Function Based Neural Network System Architecture (OAFNN) - Multilayer recurrent neural networks for synthesizing and implementing real-time linear control - Adaptive control of unknown nonlinear dynamical systems - Optimal Tracking Neural Controller techniques - Consideration of unified approximation theory and applications - Techniques for determining multivariable nonlinear model structures for dynamic systems, with a detailed treatment of relevant system model input determination
Author: Huajin Tang
Publisher: Springer Science & Business Media
ISBN: 3540692258
Category : Computers
Languages : en
Pages : 310
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Book Description
Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.
Author: Cornelius T. Leondes
Publisher:
ISBN:
Category : Automatic control
Languages : en
Pages : 360
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Book Description
Author: Vladimir M. Krasnopolsky
Publisher: Springer Science & Business Media
ISBN: 9400760736
Category : Science
Languages : en
Pages : 205
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Book Description
This book brings together a representative set of Earth System Science (ESS) applications of the neural network (NN) technique. It examines a progression of atmospheric and oceanic problems, which, from the mathematical point of view, can be formulated as complex, multidimensional, and nonlinear mappings. It is shown that these problems can be solved utilizing a particular type of NN – the multilayer perceptron (MLP). This type of NN applications covers the majority of NN applications developed in ESSs such as meteorology, oceanography, atmospheric and oceanic satellite remote sensing, numerical weather prediction, and climate studies. The major properties of the mappings and MLP NNs are formulated and discussed. Also, the book presents basic background for each introduced application and provides an extensive set of references. “This is an excellent book to learn how to apply artificial neural network methods to earth system sciences. The author, Dr. Vladimir Krasnopolsky, is a universally recognized master in this field. With his vast knowledge and experience, he carefully guides the reader through a broad variety of problems found in the earth system sciences where neural network methods can be applied fruitfully. (...) The broad range of topics covered in this book ensures that researchers/graduate students from many fields (...) will find it an invaluable guide to neural network methods.” (Prof. William W. Hsieh, University of British Columbia, Vancouver, Canada) “Vladimir Krasnopolsky has been the “founding father” of applying computation intelligence methods to environmental science; (...) Dr. Krasnopolsky has created a masterful exposition of a young, yet maturing field that promises to advance a deeper understanding of best modeling practices in environmental science.” (Dr. Sue Ellen Haupt, National Center for Atmospheric Research, Boulder, USA) “Vladimir Krasnopolsky has written an important and wonderful book on applications of neural networks to replace complex and expensive computational algorithms within Earth System Science models. He is uniquely qualified to write this book, since he has been a true pioneer with regard to many of these applications. (...) Many other examples of creative emulations will inspire not just readers interested in the Earth Sciences, but any other modeling practitioner (...) to address both theoretical and practical complex problems that may (or will!) arise in a complex system." ” (Prof. Eugenia Kalnay, University of Maryland, USA)
Author: Ayman S. El-Baz
Publisher: Academic Press
ISBN: 0128218495
Category : Science
Languages : en
Pages : 326
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Book Description
State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more. - Includes applications of neural networks, AI, machine learning, and deep learning techniques to a variety of imaging technologies - Provides in-depth technical coverage of computer-aided diagnosis (CAD), with coverage of computer-aided classification, Unified Deep Learning Frameworks, mammography, fundus imaging, optical coherence tomography, cryo-electron tomography, 3D MRI, CT, and more - Covers deep learning for several medical conditions including renal, retinal, breast, skin, and dental abnormalities, Medical Image Analysis, as well as detection, segmentation, and classification via AI
Author: M A Hussain
Publisher: World Scientific
ISBN: 178326148X
Category : Computers
Languages : en
Pages : 423
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Book Description
This book is a follow-up to the IChemE symposium on “Neural Networks and Other Learning Technologies”, held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems — modelling, estimation, control, optimisation and industrial applications.
Author: Gérard Dreyfus
Publisher: Springer Science & Business Media
ISBN: 3540288473
Category : Science
Languages : en
Pages : 509
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Book Description
Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts and edited to present a coherent and comprehensive, yet not redundant, practically oriented introduction.
