Self Organization And Associative Memory
DOWNLOAD
Download Self Organization And Associative Memory PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Self Organization And Associative Memory book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Self Organization And Associative Memory
DOWNLOAD
Author : Teuvo Kohonen
language : en
Publisher: Springer
Release Date : 2012-12-06
Self Organization And Associative Memory written by Teuvo Kohonen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
Two significant things have happened since the writing of the first edition in 1983. One of them is recent arousal of strong interest in general aspects of "neural computing", or "neural networks", as the previous neural models are nowadays called. The incentive, of course, has been to develop new com puters. Especially it may have been felt that the so-called fifth-generation computers, based on conventional logic programming, do not yet contain in formation processing principles of the same type as those encountered in the brain. All new ideas for the "neural computers" are, of course, welcome. On the other hand, it is not very easy to see what kind of restrictions there exist to their implementation. In order to approach this problem systematically, cer tain lines of thought, disciplines, and criteria should be followed. It is the pur pose of the added Chapter 9 to reflect upon such problems from a general point of view. Another important thing is a boom of new hardware technologies for dis tributed associative memories, especially high-density semiconductor circuits, and optical materials and components. The era is very close when the parallel processors can be made all-optical. Several working associative memory archi tectures, based solely on optical technologies, have been constructed in recent years. For this reason it was felt necessary to include a separate chapter (Chap. 10) which deals with the optical associative memories. Part of its con tents is taken over from the first edition.
Self Organization And Associative Memory
DOWNLOAD
Author : Teuvo Kohonen
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Self Organization And Associative Memory written by Teuvo Kohonen and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
While the present edition is bibliographically the third one of Vol. 8 of the Springer Series in Information Sciences (IS 8), the book actually stems from Vol. 17 of the series Communication and Cybernetics (CC 17), entitled Associative Memory - A System-Theoretical Approach, which appeared in 1977. That book was the first monograph on distributed associative memories, or "content-addressable memories" as they are frequently called, especially in neural-networks research. This author, however, would like to reserve the term "content-addressable memory" for certain more traditional constructs, the memory locations of which are selected by parallel search. Such devices are discussed in Vol. 1 of the Springer Series in Information Sciences, Content-Addressable Memories. This third edition of IS 8 is rather similar to the second one. Two new discussions have been added: one to the end of Chap. 5, and the other (the L VQ 2 algorithm) to the end of Chap. 7. Moreover, the convergence proof in Sect. 5.7.2 has been revised.
Self Organization In Optical Systems And Applications In Information Technology
DOWNLOAD
Author : Mikhail A. Vorontsov
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Self Organization In Optical Systems And Applications In Information Technology written by Mikhail A. Vorontsov and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Science categories.
Contrary to monographs on non-linear optics this book concentrates on problems of self-organization in various important contexts. The reader learns how patterns in non-linear optical systems are created and what theoretical methods can be applied to describe them. Next, various aspects of pattern formation such as associative memory, information processing, spatio-temporal instability, photo refraction, and so on are treated. The book addresses graduate students and researchers in physics and optical engineering.
Introduction To Neural And Cognitive Modeling
DOWNLOAD
Author : Daniel S. Levine
language : en
Publisher: Routledge
Release Date : 2018-10-26
Introduction To Neural And Cognitive Modeling written by Daniel S. Levine and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-26 with Psychology categories.
This textbook provides a general introduction to the field of neural networks. Thoroughly revised and updated from the previous editions of 1991 and 2000, the current edition concentrates on networks for modeling brain processes involved in cognitive and behavioral functions. Part one explores the philosophy of modeling and the field’s history starting from the mid-1940s, and then discusses past models of associative learning and of short-term memory that provide building blocks for more complex recent models. Part two of the book reviews recent experimental findings in cognitive neuroscience and discusses models of conditioning, categorization, category learning, vision, visual attention, sequence learning, behavioral control, decision making, reasoning, and creativity. The book presents these models both as abstract ideas and through examples and concrete data for specific brain regions. The book includes two appendices to help ground the reader: one reviewing the mathematics used in network modeling, and a second reviewing basic neuroscience at both the neuron and brain region level. The book also includes equations, practice exercises, and thought experiments.
Self Adaptive Systems For Machine Intelligence
DOWNLOAD
Author : Haibo He
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-15
Self Adaptive Systems For Machine Intelligence written by Haibo He and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-15 with Computers categories.
This book will advance the understanding and application of self-adaptive intelligent systems; therefore it will potentially benefit the long-term goal of replicating certain levels of brain-like intelligence in complex and networked engineering systems. It will provide new approaches for adaptive systems within uncertain environments. This will provide an opportunity to evaluate the strengths and weaknesses of the current state-of-the-art of knowledge, give rise to new research directions, and educate future professionals in this domain. Self-adaptive intelligent systems have wide applications from military security systems to civilian daily life. In this book, different application problems, including pattern recognition, classification, image recovery, and sequence learning, will be presented to show the capability of the proposed systems in learning, memory, and prediction. Therefore, this book will also provide potential new solutions to many real-world applications.
Inference And Learning From Data Volume 3
DOWNLOAD
Author : Ali H. Sayed
language : en
Publisher: Cambridge University Press
Release Date : 2022-12-22
Inference And Learning From Data Volume 3 written by Ali H. Sayed and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-22 with Technology & Engineering categories.
This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
Implementation Techniques
DOWNLOAD
Author : Cornelius T. Leondes
language : en
Publisher: Academic Press
Release Date : 1998-02-09
Implementation Techniques written by Cornelius T. Leondes and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-02-09 with Computers categories.
This volume covers practical and effective implementation techniques, including recurrent methods, Boltzmann machines, constructive learning with methods for the reduction of complexity in neural network systems, modular systems, associative memory, neural network design based on the concept of the Inductive Logic Unit, and a comprehensive treatment of implementations in the area of data classification. Numerous examples enhance the text. Practitioners, researchers,and students in engineering and computer science will find Implementation Techniques a comprehensive and powerful reference. - Recurrent methods - Boltzmann machines - Constructive learning with methods for the reduction of complexity in neural network systems - Modular systems - Associative memory - Neural network design based on the concept of the Inductive Logic Unit - Data classification - Integrated neuron model systems that function as programmable rational approximators
Thinking In Complexity
DOWNLOAD
Author : Klaus Mainzer
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Thinking In Complexity written by Klaus Mainzer and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Science categories.
Since the first edition sold out in less than a year, we now present the revised second edition of Mainzer's popular book. The theory of nonlinear complex systems has become a successful problem-solving approach in the natural sciences from laser physics, quantum chaos, and meteorology to computer simulations of cell growth in biology. It is now recognized that many of our social, ecological, and political problems are also of a global, complex, and nonlinear nature. And one of the most exciting contemporary topics is the idea that even the human mind is governed largely by the nonlinear dynamics of complex systems. In this wide-ranging but concise treatment, Prof. Mainzer discusses, in a nontechnical language, the common framework behind these endeavors. Emphasis is given to the evolution of new structures in natural and cultural systems and we see clearly how the new integrative approach can give insights not available from traditional reductionistic methods.
Neural Networks And Statistical Learning
DOWNLOAD
Author : Ke-Lin Du
language : en
Publisher: Springer Nature
Release Date : 2019-09-12
Neural Networks And Statistical Learning written by Ke-Lin Du and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-12 with Mathematics categories.
This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.
Kohonen Maps
DOWNLOAD
Author : E. Oja
language : en
Publisher: Elsevier
Release Date : 1999-07-02
Kohonen Maps written by E. Oja and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-07-02 with Computers categories.
The Self-Organizing Map, or Kohonen Map, is one of the most widely used neural network algorithms, with thousands of applications covered in the literature. It was one of the strong underlying factors in the popularity of neural networks starting in the early 80's. Currently this method has been included in a large number of commercial and public domain software packages. In this book, top experts on the SOM method take a look at the state of the art and the future of this computing paradigm.The 30 chapters of this book cover the current status of SOM theory, such as connections of SOM to clustering, classification, probabilistic models, and energy functions. Many applications of the SOM are given, with data mining and exploratory data analysis the central topic, applied to large databases of financial data, medical data, free-form text documents, digital images, speech, and process measurements. Biological models related to the SOM are also discussed.