Download or read book Neural Network Models for Optical Computing written by Society of Photo-optical Instrumentation Engineers and published by . This book was released on 1988 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural network models for optical computing written by Ravindra A. Athale and published by . This book was released on 1988 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Network Models for Optical Computing written by Ravindra A. Athale and published by . This book was released on 1988 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Photonic Neural Networks with Spatiotemporal Dynamics written by Hideyuki Suzuki and published by Springer Nature. This book was released on 2023-10-16 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents an overview of recent advances in photonic neural networks with spatiotemporal dynamics. The computing and implementation paradigms presented in this book are outcomes of interdisciplinary studies by collaborative researchers from the three fields of nonlinear mathematical science, information photonics, and integrated systems engineering. This book offers novel multidisciplinary viewpoints on photonic neural networks, illustrating recent advances in three types of computing methodologies: fluorescence energy transfer computing, spatial-photonic spin system, and photonic reservoir computing. The book consists of four parts: Part I introduces the backgrounds of optical computing and neural network dynamics; Part II presents fluorescence energy transfer computing, a novel computing technology based on nanoscale networks of fluorescent particles; Parts III and IV review the models and implementation of spatial-photonic spin systems and photonic reservoir computing, respectively. These contents are beneficial to researchers in a broad range of fields, including information science, mathematical science, applied physics, and engineering, to better understand the novel computing concepts of photonic neural networks with spatiotemporal dynamics.
Download or read book Photonic Reservoir Computing written by Daniel Brunner and published by Walter de Gruyter GmbH & Co KG. This book was released on 2019-07-08 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.
Download or read book Optical Computing and Neural Networks written by Hua-Kuang Liu and published by . This book was released on 1992 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selected Papers on Optical Neural Networks written by Suganda Jutamulia and published by SPIE-International Society for Optical Engineering. This book was released on 1994 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optical Signal Processing Computing and Neural Networks written by Frances T. S. Yu and published by Wiley-Interscience. This book was released on 1992-11-19 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, optical computing and optical neural networks research has enriched the field originally known as optical signal processing. Optical Signal Processing, Computing, and Neural Networks is a self-contained textbook that offers an introductory survey which examines photonics, linear and nonlinear signal processing, and numerical, symbolic, and neural computing. This comprehensive sourcebook is a basic text for students who lack an intensive background in optic, electromagnetic, computer, and neural network theories. It will also serve as a working reference for optical physicists and engineers involved in current research and development of modern optical signal processing that includes optical computing and neural networks. The first chapter of this book contains the basic coherent theory and concepts of optical transformation. The second chapter introduces the fundamental concept of optical signal processing and its architectures. The third chapter presents selected applications in coherent optics while the fourth chapter discusses white-light processing and its applications. The advances of spatial-light modulators are discussed as well as hybrid-optical architectures using spatial-light modulators in later chapters. Applications of photorefractive crystals in optical signal processing are presented in chapter 7. Digital-optical computing is described in chapter 8 while optical neural networks and their architectures, designs, and models are thoroughly covered in chapter 9. Examples and experimental results are included throughout the book to emphasize the concepts. Chapters include problem sets, 330 throughout, that reinforce key elements in the text.
Download or read book Neural Networks written by Richard Kendall Miller and published by . This book was released on 1990 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Theoretical Investigation of Optical Computing Based on Neural Network Models written by Yaser S. Abu-Mostafa and published by . This book was released on 1987 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is difficult to find good mathematical models for many natural problems such as pattern recognition. Not only does this difficulty preclude finding good solutions for these problems, but it also precludes estimating their complexity using the standard tools of the theory oc computational complexity (Traub, 1985). Part of the difficulty can be traced to symptoms such as ill-definition, fuzziness, and inexactness. However, the difficulty of modeling these problems may be inherent in some cases. Keywords: Photorefractive crystals; Adaptive optical networks; Connectivity; Entropy; Holograms.
Download or read book Neuromorphic Photonics written by Paul R. Prucnal and published by CRC Press. This book was released on 2017-05-08 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.
Download or read book Massively Parallel Optical and Neural Computing in the United States written by Gilbert Kalb and published by IOS Press. This book was released on 1992 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: A survey of products and research projects in the field of highly parallel, optical and neural computers in the USA. It covers operating systems, language projects and market analysis, as well as optical computing devices and optical connections of electronic parts.
Download or read book Photonics for Processors Neural Networks and Memories written by Joseph LeFevre Horner and published by SPIE-International Society for Optical Engineering. This book was released on 1993 with total page 688 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book An Introduction to Optics in Computers written by Henri H. Arsenault and published by SPIE Press. This book was released on 1992 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume surveys the entire field of optical computing. The emphasis is on breadth of coverage. The book is descriptive, the authors minimize the use of mathematics, and it is therefore most suitable for those who require an overall view of what is going on in this field. A detailed comparison is given of the capabilities of electronics and optics, and the degree to which these capabilities have been achieved is indicated. Other areas of focus include optical computing architectures, components and technologies, optical interconnects, and optical neural nets. Approximately 300 references to key works in the field are included.
Download or read book Neural Computing An Introduction written by R Beale and published by CRC Press. This book was released on 1990-01-01 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural computing is one of the most interesting and rapidly growing areas of research, attracting researchers from a wide variety of scientific disciplines. Starting from the basics, Neural Computing covers all the major approaches, putting each in perspective in terms of their capabilities, advantages, and disadvantages. The book also highlights the applications of each approach and explores the relationships among models developed and between the brain and its function. A comprehensive and comprehensible introduction to the subject, this book is ideal for undergraduates in computer science, physicists, communications engineers, workers involved in artificial intelligence, biologists, psychologists, and physiologists.
Download or read book Optical Computing Based on the Hopfield Model for Neural Nets written by N. H. Hart and published by . This book was released on 1986 with total page 65 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural net models and their analogs present a new approach to signal processing that is collective, robust, and fault tolerant. The collective nature of processing means also high throughput where bandwidth or speed of the processing elements i.e., temporal degrees of freedom are traded by spatial degrees of freedom via massive interconnectivity of the elements (neurons) in the network. As a result neural nets can solve computationally extensive problems like those encountered for example in optimization, nearest neighbor search, inverse scattering in a matter of a few time-constants of the processing elements. In biological systems (the brain in particular) this is of the order of a tens of milliseconds since neurons operate with electronic conduction and hence can be made considerably faster with time constants approaching nano-seconds. Collective processing such artificial nets can therefore be extremely fast. The remarkable ability of neural nets in handling sketchy (erroneous or incomplete) information and their fault tolerance (graceful degradation in performance with element failure) make them particularly attractive in pattern recognition, robotics, and autonomous system intended to operate virtually unattended for long time periods.
Download or read book Optical Computing Research written by Joseph W. Goodman and published by . This book was released on 1988 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt: The research focused on understanding the global as well as local properties of the neural network model. Global properties are the dynamics of the network, convergency properties, computational power and capacity. Local properties mean the theory of threshold logic elements, the basic building blocks of the network. Investigated is the relation between error-correcting codes and neural networks. The motivation was that a neural network model can be viewed as a decoder. The stable states correspond to codewords, the probe vector corresponds to the received vector, and convergence to the closest stable state corresponds to Maximum Likelihood Decoding (MLD). Several natural ways were found for connecting the concepts of error correcting codes with the concept of neural networks. The MLD problem in a linear block code is equivalent to finding the global maximum of the energy function of a neural network that can be easily constructed knowing the basis set of the code. (jhd).
