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Recursive Neural Networks For Associative Memory
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Book Synopsis Recursive Neural Networks for Associative Memory by : Yves Kamp
Download or read book Recursive Neural Networks for Associative Memory written by Yves Kamp and published by . This book was released on 1990-12-28 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: A discussion of the different problems which arise in the analysis and design of discrete time and discrete valued recursive networks. It is the aim of this book to present a structured introduction to these networks, which, in spite of their simple architecture, exhibit complex behaviours.
Book Synopsis Self-Organization and Associative Memory by : Teuvo Kohonen
Download or read book Self-Organization and Associative Memory written by Teuvo Kohonen and published by Springer. This book was released on 2012-12-06 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Neural Network Natural Language Translation Using Recursive Auto-associative Memory by : Jasmine Yi-Fen Hsiao
Download or read book Neural Network Natural Language Translation Using Recursive Auto-associative Memory written by Jasmine Yi-Fen Hsiao and published by . This book was released on 1996 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Convergence Analysis of Recurrent Neural Networks by : Zhang Yi
Download or read book Convergence Analysis of Recurrent Neural Networks written by Zhang Yi and published by Springer. This book was released on 2013-09-14 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the outstanding and pioneering research work of Hopfield on recurrent neural networks (RNNs) in the early 80s of the last century, neural networks have rekindled strong interests in scientists and researchers. Recent years have recorded a remarkable advance in research and development work on RNNs, both in theoretical research as weIl as actual applications. The field of RNNs is now transforming into a complete and independent subject. From theory to application, from software to hardware, new and exciting results are emerging day after day, reflecting the keen interest RNNs have instilled in everyone, from researchers to practitioners. RNNs contain feedback connections among the neurons, a phenomenon which has led rather naturally to RNNs being regarded as dynamical systems. RNNs can be described by continuous time differential systems, discrete time systems, or functional differential systems, and more generally, in terms of non linear systems. Thus, RNNs have to their disposal, a huge set of mathematical tools relating to dynamical system theory which has tumed out to be very useful in enabling a rigorous analysis of RNNs.
Download or read book Associative Memory written by T. Kohonen and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: About the Scope of This Text This book contains two types of material ~ first, the many divergent and often diffuse meanings given to the concepts of association, associative memory, and associative recaZZ are expounded. A review of this kind was felt necessary because there apparently does not exist any single monograph which could serve as a reference to these topics. But the presentation of the main body of this text is motivated by quite other reasons: in recent years, plenty of interesting mathematical and system-theoretical material has been published which makes it possible to gain a view of associative memory which is different from the conventional abstract and computationally oriented approaches. It seems that the basic operation of associative memory, the storage of information together with the relations or links between the data items, and the selective recall of stored information relative to a piece of key or cue information presented, is not restricted to certain computer-technological implementations but can also be reflected in more general mathematically describable processes in certain physical or other systems, especially in their adaptive state changes. It further seems that some generally known forms of associative memory, namely, certain computer technological artifacts, or abstract systems of concepts or data, are in fact special representations of a class of processes characterized as associative memory.
Book Synopsis Recurrent Neural Networks by : Fouad Sabry
Download or read book Recurrent Neural Networks written by Fouad Sabry and published by One Billion Knowledgeable. This book was released on 2023-06-26 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: What Is Recurrent Neural Networks An artificial neural network that belongs to the class known as recurrent neural networks (RNNs) is one in which the connections between its nodes can form a cycle. This allows the output of some nodes to have an effect on subsequent input to the very same nodes. Because of this, it is able to display temporally dynamic behavior. RNNs are a descendant of feedforward neural networks and have the ability to use their internal state (memory) to process input sequences of varying lengths. Because of this, they are suitable for applications such as speech recognition and unsegmented, connected handwriting recognition. Theoretically, recurrent neural networks are considered to be Turing complete since they are able to execute arbitrary algorithms and interpret arbitrary sequences of inputs. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Recurrent neural network Chapter 2: Artificial neural network Chapter 3: Backpropagation Chapter 4: Long short-term memory Chapter 5: Types of artificial neural networks Chapter 6: Deep learning Chapter 7: Vanishing gradient problem Chapter 8: Bidirectional recurrent neural networks Chapter 9: Gated recurrent unit Chapter 10: Attention (machine learning) (II) Answering the public top questions about recurrent neural networks. (III) Real world examples for the usage of recurrent neural networks in many fields. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of recurrent neural networks. What Is Artificial Intelligence Series The Artificial Intelligence book series provides comprehensive coverage in over 200 topics. Each ebook covers a specific Artificial Intelligence topic in depth, written by experts in the field. The series aims to give readers a thorough understanding of the concepts, techniques, history and applications of artificial intelligence. Topics covered include machine learning, deep learning, neural networks, computer vision, natural language processing, robotics, ethics and more. The ebooks are written for professionals, students, and anyone interested in learning about the latest developments in this rapidly advancing field. The artificial intelligence book series provides an in-depth yet accessible exploration, from the fundamental concepts to the state-of-the-art research. With over 200 volumes, readers gain a thorough grounding in all aspects of Artificial Intelligence. The ebooks are designed to build knowledge systematically, with later volumes building on the foundations laid by earlier ones. This comprehensive series is an indispensable resource for anyone seeking to develop expertise in artificial intelligence.
Book Synopsis Parallel Models of Associative Memory by : Geoffrey E. Hinton
Download or read book Parallel Models of Associative Memory written by Geoffrey E. Hinton and published by Psychology Press. This book was released on 1989-01-01 with total page 339 pages. Available in PDF, EPUB and Kindle. Book excerpt: This update of the 1981 classic on neural networks includes new commentaries by the authors that show how the original ideas are related to subsequent developments. As researchers continue to uncover ways of applying the complex information processing abilities of neural networks, they give these models an exciting future which may well involve revolutionary developments in understanding the brain and the mind -- developments that may allow researchers to build adaptive intelligent machines. The original chapters show where the ideas came from and the new commentaries show where they are going.
Book Synopsis Associative Memory in a Recurrent Neural Network by :
Download or read book Associative Memory in a Recurrent Neural Network written by and published by . This book was released on 2001 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is part of a project with the long-term goal of demonstrating self-organized, goal-driven adaptive learning in a simulated mobile vehicle with multiple sensors. The research focusses on recurrent neural networks (RNN) since they appear to have the potential to generate some of the characteristics believed to be required for the creation of intelligent behaviour in a machine. The report first provides details of the RNN structure and learning rules, the latter involving both Hebbian (D.O. Hebb, 1949) and feedback learning. Results are presented for trials using ten sensor images as a training set for the RNN. The main questions investigated are: can a pattern & its identification code be stored by the simultaneous presentation of two sensor image streams during training; and can an identification be recovered from the code memory array by the single presentation of a pattern in the image sensor stream. The findings show the relationship between RNN memory capacity and the number of training patterns.
Book Synopsis Synthesis of Neural Networks for Associative Memories by : Zanjun Lu
Download or read book Synthesis of Neural Networks for Associative Memories written by Zanjun Lu and published by . This book was released on 1999 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Recurrent Neural Networks by : Larry Medsker
Download or read book Recurrent Neural Networks written by Larry Medsker and published by CRC Press. This book was released on 1999-12-20 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: With existent uses ranging from motion detection to music synthesis to financial forecasting, recurrent neural networks have generated widespread attention. The tremendous interest in these networks drives Recurrent Neural Networks: Design and Applications, a summary of the design, applications, current research, and challenges of this subfield of artificial neural networks. This overview incorporates every aspect of recurrent neural networks. It outlines the wide variety of complex learning techniques and associated research projects. Each chapter addresses architectures, from fully connected to partially connected, including recurrent multilayer feedforward. It presents problems involving trajectories, control systems, and robotics, as well as RNN use in chaotic systems. The authors also share their expert knowledge of ideas for alternate designs and advances in theoretical aspects. The dynamical behavior of recurrent neural networks is useful for solving problems in science, engineering, and business. This approach will yield huge advances in the coming years. Recurrent Neural Networks illuminates the opportunities and provides you with a broad view of the current events in this rich field.
Book Synopsis Neural Network Dynamical Systems for Associative Memory and Control by : Mark Alan Copeland
Download or read book Neural Network Dynamical Systems for Associative Memory and Control written by Mark Alan Copeland and published by . This book was released on 1993 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis A Locally-distributed Associative Memory Network by : Robert Jacob Baron
Download or read book A Locally-distributed Associative Memory Network written by Robert Jacob Baron and published by . This book was released on 1968 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: The principal purpose of this report is to propose a mathematical model for an associative memory network. A network of mathematical neurons is presented which is capable of storing the information patterns which arrive through specific collections of neurons. The neurons of the model resemble biological neurons in many ways, and it is shown that in a network the size of the cerebral cortex, there is sufficient capacity to store the images accumulated during an average human lifetime. The storage network is based on the principle of 'matched filtering.' The recognition of current information is accomplished by crosscorrelating the current input information with previously stored information. This crosscorrelation occurs simultaneously at every storage location in the memory network whenever an input pattern arrives at the memory network. The recalled pattern from a particular memory location is a copy of the information stored within that memory location. Computer simulations of the memory network indicate that for patterns comprised of 'fine lines, ' the recognition signal is stronger than for patterns composed of 'broad lines.' Simulations also show that the memory network functions adequately well even if there is a large amount of background noise. (Author).
Book Synopsis Modular Context-dependent Functional Networks for Associative Memory by : Chandrika Sagar
Download or read book Modular Context-dependent Functional Networks for Associative Memory written by Chandrika Sagar and published by . This book was released on 2011 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: All mental function - perception, cognition or action - is, ultimately, based on memory and its appropriate recall. The question of how the brain learns memories and their associations has been one of the most debated and interesting topics of research by cognitive psychologists and neuroscientists. Experiments show that human memory is largely associative, working through association between entities seen in the real world. In such a memory, recall occurs when the system is presented with a partial cue for the content, which then triggers the rest of the memory by association. A very important aspect of human memory is that associations are highly context-dependent, allowing the brain to represent a very large body of knowledge concisely through different combinations of a relatively small set of memories. This can also be seen as the basis of the brain's ability to generate novel ideas. The most successful computational models for associative memory are based on attractor dynamics in recurrent neural networks. However, relatively little work has been done on context-dependent associative memory. This thesis presents modular recurrent neural network architecture for context-dependent associative memory (CDAM) that provides a natural and biologically plausible way to represent context-dependence in the network dynamics. The simulations shown indicate that this system is capable of both exploitation (recalling previously learned memories in a context-appropriate way) and exploration (generating novel but sensible "memories" that can be seen as representing new ideas.) The role of noise in both exploitation and exploration is an especially intriguing issue, and is investigated systematically in the thesis.
Book Synopsis Associative memory by : Mark Kenji Brightwell
Download or read book Associative memory written by Mark Kenji Brightwell and published by . This book was released on 1986 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Synthesis of Neural Network Based Associative Memories by : H. M. Wabgaonkar
Download or read book Synthesis of Neural Network Based Associative Memories written by H. M. Wabgaonkar and published by . This book was released on 1991 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Associative Memory and Artificial Neural Networks by : Joseph B. Wyatt
Download or read book Associative Memory and Artificial Neural Networks written by Joseph B. Wyatt and published by . This book was released on 1989 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Performance of Neural Associative Memories for Character Recognition and Associative Database Retrieval by : Nikolaos Vassilas
Download or read book Performance of Neural Associative Memories for Character Recognition and Associative Database Retrieval written by Nikolaos Vassilas and published by . This book was released on 1990 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt:
