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Theory And Applications Of Neural Networks
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Book Synopsis Process Neural Networks by : Xingui He
Download or read book Process Neural Networks written by Xingui He and published by Springer Science & Business Media. This book was released on 2010-07-05 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Handbook of Brain Theory and Neural Networks by : Michael A. Arbib
Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press. This book was released on 2003 with total page 1328 pages. Available in PDF, EPUB and Kindle. Book excerpt: This second edition presents the enormous progress made in recent years in the many subfields related to the two great questions : how does the brain work? and, How can we build intelligent machines? This second edition greatly increases the coverage of models of fundamental neurobiology, cognitive neuroscience, and neural network approaches to language. (Midwest).
Book Synopsis Principal Component Neural Networks by : K. I. Diamantaras
Download or read book Principal Component Neural Networks written by K. I. Diamantaras and published by Wiley-Interscience. This book was released on 1996-03-08 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematically explores the relationship between principal component analysis (PCA) and neural networks. Provides a synergistic examination of the mathematical, algorithmic, application and architectural aspects of principal component neural networks. Using a unified formulation, the authors present neural models performing PCA from the Hebbian learning rule and those which use least squares learning rules such as back-propagation. Examines the principles of biological perceptual systems to explain how the brain works. Every chapter contains a selected list of applications examples from diverse areas.
Book Synopsis Fuzzy Neural Network Theory and Application by : Puyin Liu
Download or read book Fuzzy Neural Network Theory and Application written by Puyin Liu and published by World Scientific. This book was released on 2004 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to help the reader grasp the underlying theory. This is a valuable reference for scientists and engineers working in mathematics, computer science, control or other fields related to information processing. It can also be used as a textbook for graduate courses in applied mathematics, computer science, automatic control and electrical engineering. Contents: Fuzzy Neural Networks for Storing and Classifying; Fuzzy Associative Memory OCo Feedback Networks; Regular Fuzzy Neural Networks; Polygonal Fuzzy Neural Networks; Approximation Analysis of Fuzzy Systems; Stochastic Fuzzy Systems and Approximations; Application of FNN to Image Restoration. Readership: Scientists, engineers and graduate students in applied mathematics, computer science, automatic control and information processing."
Book Synopsis Introduction to Neural Networks by : Jeannette Lawrence
Download or read book Introduction to Neural Networks written by Jeannette Lawrence and published by . This book was released on 1994 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Artificial Intelligence Systems Based on Hybrid Neural Networks by : Michael Zgurovsky
Download or read book Artificial Intelligence Systems Based on Hybrid Neural Networks written by Michael Zgurovsky and published by Springer Nature. This book was released on 2020-09-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended for specialists as well as students and graduate students in the field of artificial intelligence, robotics and information technology. It is will also appeal to a wide range of readers interested in expanding the functionality of artificial intelligence systems. One of the pressing problems of modern artificial intelligence systems is the development of integrated hybrid systems based on deep learning. Unfortunately, there is currently no universal methodology for developing topologies of hybrid neural networks (HNN) using deep learning. The development of such systems calls for the expansion of the use of neural networks (NS) for solving recognition, classification and optimization problems. As such, it is necessary to create a unified methodology for constructing HNN with a selection of models of artificial neurons that make up HNN, gradually increasing the complexity of their structure using hybrid learning algorithms.
Book Synopsis Evolutionary Algorithms and Neural Networks by : Seyedali Mirjalili
Download or read book Evolutionary Algorithms and Neural Networks written by Seyedali Mirjalili and published by Springer. This book was released on 2018-06-26 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At first, the book offers a literature review of several well-regarded evolutionary algorithms, including particle swarm and ant colony optimization, genetic algorithms and biogeography-based optimization. It then proposes evolutionary version of several types of neural networks such as feed forward neural networks, radial basis function networks, as well as recurrent neural networks and multi-later perceptron. Most of the challenges that have to be addressed when training artificial neural networks using evolutionary algorithms are discussed in detail. The book also demonstrates the application of the proposed algorithms for several purposes such as classification, clustering, approximation, and prediction problems. It provides a tutorial on how to design, adapt, and evaluate artificial neural networks as well, and includes source codes for most of the proposed techniques as supplementary materials.
Author :Alexander I. Galushkin Publisher :Springer Science & Business Media ISBN 13 :3540481257 Total Pages :396 pages Book Rating :4.5/5 (44 download)
Book Synopsis Neural Networks Theory by : Alexander I. Galushkin
Download or read book Neural Networks Theory written by Alexander I. Galushkin and published by Springer Science & Business Media. This book was released on 2007-10-29 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by a leader in neural network theory in Russia, uses mathematical methods in combination with complexity theory, nonlinear dynamics and optimization. It details more than 40 years of Soviet and Russian neural network research and presents a systematized methodology of neural networks synthesis. The theory is expansive: covering not just traditional topics such as network architecture but also neural continua in function spaces as well.
Author :Mathukumalli Vidyasagar Publisher :Springer Science & Business Media ISBN 13 :1447137485 Total Pages :498 pages Book Rating :4.4/5 (471 download)
Book Synopsis Learning and Generalisation by : Mathukumalli Vidyasagar
Download or read book Learning and Generalisation written by Mathukumalli Vidyasagar and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 498 pages. Available in PDF, EPUB and Kindle. Book excerpt: How does a machine learn a new concept on the basis of examples? This second edition takes account of important new developments in the field. It also deals extensively with the theory of learning control systems, now comparably mature to learning of neural networks.
Book Synopsis Neural Networks: Computational Models and Applications by : Huajin Tang
Download or read book Neural Networks: Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Artificial Neural Networks by : P.J. Braspenning
Download or read book Artificial Neural Networks written by P.J. Braspenning and published by Springer Science & Business Media. This book was released on 1995-06-02 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents carefully revised versions of tutorial lectures given during a School on Artificial Neural Networks for the industrial world held at the University of Limburg in Maastricht, Belgium. The major ANN architectures are discussed to show their powerful possibilities for empirical data analysis, particularly in situations where other methods seem to fail. Theoretical insight is offered by examining the underlying mathematical principles in a detailed, yet clear and illuminating way. Practical experience is provided by discussing several real-world applications in such areas as control, optimization, pattern recognition, software engineering, robotics, operations research, and CAM.
Book Synopsis Cellular Neural Networks by : Angela Slavova
Download or read book Cellular Neural Networks written by Angela Slavova and published by Nova Publishers. This book was released on 2004 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with new theoretical results for studyingCellular Neural Networks (CNNs) concerning its dynamical behavior. Newaspects of CNNs' applications are developed for modelling of somefamous nonlinear partial differential equations arising in biology, genetics, neurophysiology, physics, ecology, etc. The analysis ofCNNs' models is based on the harmonic balance method well known incontrol theory and in the study of electronic oscillators. Suchphenomena as hysteresis, bifurcation and chaos are studied for CNNs.The topics investigated in the book involve several scientificdisciplines, such as dynamical systems, applied mathematics, mathematical modelling, information processing, biology andneurophysiology. The reader will find comprehensive discussion on thesubject as well as rigorous mathematical analyses of networks ofneurons from the view point of dynamical systems. The text is writtenas a textbook for senior undergraduate and graduate students inapplied mathematics. Providing a summary of recent results on dynamicsand modelling of CNNs, the book will also be of interest to allresearchers in the area.
Book Synopsis Artificial Neural Networks by : Dan W. Patterson
Download or read book Artificial Neural Networks written by Dan W. Patterson and published by . This book was released on 1996 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive tutorial on artifical neural networks covers all the important neural network architectures as well as the most recent theory--e.g., pattern recognition, statistical theory, and other mathematical prerequisites. A broad range of applications is provided for each of the architectures.
Book Synopsis Theory and Applications of Neural Networks by : J.G. Taylor
Download or read book Theory and Applications of Neural Networks written by J.G. Taylor and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the papers from the first British Neural Network Society meeting held at Queen Elizabeth Hall, King's College, London on 18--20 April 1990. The meeting was sponsored by the London Mathemati cal Society. The papers include introductory tutorial lectures, invited, and contributed papers. The invited contributions were given by experts from the United States, Finland, Denmark, Germany and the United Kingdom. The majority of the contributed papers came from workers in the United Kingdom. The first day was devoted to tutorials. Professor Stephen Grossberg was a guest speaker on the first day giving a thorough introduction to his Adaptive Resonance Theory of neural networks. Subsequent tutorials on the first day covered dynamical systems and neural networks, realistic neural modelling, pattern recognition using neural networks, and a review of hardware for neural network simulations. The contributed papers, given on the second day, demonstrated the breadth of interests of workers in the field. They covered topics in pattern recognition, multi-layer feedforward neural networks, network dynamics, memory and learning. The ordering of the papers in this volume is as they were given at the meeting. On the final day talks were given by Professor Kohonen (on self organising maps), Professor Kurten (on the dynamics of random and structured nets) and Professor Cotterill (on modelling the visual cortex). Dr A. Mayes presented a paper on various models for amnesia. The editors have taken the opportunity to include a paper of their own which was not presented at the meeting.
Book Synopsis Neural Networks and Fuzzy Systems by : Shigeo Abe
Download or read book Neural Networks and Fuzzy Systems written by Shigeo Abe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks and Fuzzy Systems: Theory and Applications discusses theories that have proven useful in applying neural networks and fuzzy systems to real world problems. The book includes performance comparison of neural networks and fuzzy systems using data gathered from real systems. Topics covered include the Hopfield network for combinatorial optimization problems, multilayered neural networks for pattern classification and function approximation, fuzzy systems that have the same functions as multilayered networks, and composite systems that have been successfully applied to real world problems. The author also includes representative neural network models such as the Kohonen network and radial basis function network. New fuzzy systems with learning capabilities are also covered. The advantages and disadvantages of neural networks and fuzzy systems are examined. The performance of these two systems in license plate recognition, a water purification plant, blood cell classification, and other real world problems is compared.
Book Synopsis The Handbook of Brain Theory and Neural Networks by : Michael A. Arbib
Download or read book The Handbook of Brain Theory and Neural Networks written by Michael A. Arbib and published by MIT Press (MA). This book was released on 1998 with total page 1118 pages. Available in PDF, EPUB and Kindle. Book excerpt: Choice Outstanding Academic Title, 1996. In hundreds of articles by experts from around the world, and in overviews and "road maps" prepared by the editor, The Handbook of Brain Theory and Neural Networks charts the immense progress made in recent years in many specific areas related to great questions: How does the brain work? How can we build intelligent machines? While many books discuss limited aspects of one subfield or another of brain theory and neural networks, the Handbook covers the entire sweep of topics—from detailed models of single neurons, analyses of a wide variety of biological neural networks, and connectionist studies of psychology and language, to mathematical analyses of a variety of abstract neural networks, and technological applications of adaptive, artificial neural networks. Expository material makes the book accessible to readers with varied backgrounds while still offering a clear view of the recent, specialized research on specific topics.
Book Synopsis From Statistics to Neural Networks by : Vladimir Cherkassky
Download or read book From Statistics to Neural Networks written by Vladimir Cherkassky and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: The NATO Advanced Study Institute From Statistics to Neural Networks, Theory and Pattern Recognition Applications took place in Les Arcs, Bourg Saint Maurice, France, from June 21 through July 2, 1993. The meeting brought to gether over 100 participants (including 19 invited lecturers) from 20 countries. The invited lecturers whose contributions appear in this volume are: L. Almeida (INESC, Portugal), G. Carpenter (Boston, USA), V. Cherkassky (Minnesota, USA), F. Fogelman Soulie (LRI, France), W. Freeman (Berkeley, USA), J. Friedman (Stanford, USA), F. Girosi (MIT, USA and IRST, Italy), S. Grossberg (Boston, USA), T. Hastie (AT&T, USA), J. Kittler (Surrey, UK), R. Lippmann (MIT Lincoln Lab, USA), J. Moody (OGI, USA), G. Palm (U1m, Germany), B. Ripley (Oxford, UK), R. Tibshirani (Toronto, Canada), H. Wechsler (GMU, USA), C. Wellekens (Eurecom, France) and H. White (San Diego, USA). The ASI consisted of lectures overviewing major aspects of statistical and neural network learning, their links to biological learning and non-linear dynamics (chaos), and real-life examples of pattern recognition applications. As a result of lively interactions between the participants, the following topics emerged as major themes of the meeting: (1) Unified framework for the study of Predictive Learning in Statistics and Artificial Neural Networks (ANNs); (2) Differences and similarities between statistical and ANN methods for non parametric estimation from examples (learning); (3) Fundamental connections between artificial learning systems and biological learning systems.
