Download or read book CIRP Encyclopedia of Production Engineering written by The International Academy for Produ and published by Springer. This book was released on 2014-04-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The CIRP Encyclopedia covers the state-of-art of advanced technologies, methods and models for production, production engineering and logistics. While the technological and operational aspects are in the focus, economical aspects are addressed too. The entries for a wide variety of terms were reviewed by the CIRP-Community, representing the highest standards in research. Thus, the content is not only evaluated internationally on a high scientific level but also reflects very recent developments.
Download or read book Geometry of Deep Learning written by Jong Chul Ye and published by Springer Nature. This book was released on 2022-01-05 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems. Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
Download or read book Principles of Artificial Neural Networks written by Daniel Graupe and published by World Scientific. This book was released on 1997-05-01 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook is intended for a first-year graduate course on Artificial Neural Networks. It assumes no prior background in the subject and is directed to MS students in electrical engineering, computer science and related fields, with background in at least one programming language or in a programming tool such as Matlab, and who have taken the basic undergraduate classes in systems or in signal processing.
Download or read book Handbook of Natural Computing written by Grzegorz Rozenberg and published by Springer. This book was released on 2012-07-09 with total page 2052 pages. Available in PDF, EPUB and Kindle. Book excerpt: Natural Computing is the field of research that investigates both human-designed computing inspired by nature and computing taking place in nature, i.e., it investigates models and computational techniques inspired by nature and also it investigates phenomena taking place in nature in terms of information processing. Examples of the first strand of research covered by the handbook include neural computation inspired by the functioning of the brain; evolutionary computation inspired by Darwinian evolution of species; cellular automata inspired by intercellular communication; swarm intelligence inspired by the behavior of groups of organisms; artificial immune systems inspired by the natural immune system; artificial life systems inspired by the properties of natural life in general; membrane computing inspired by the compartmentalized ways in which cells process information; and amorphous computing inspired by morphogenesis. Other examples of natural-computing paradigms are molecular computing and quantum computing, where the goal is to replace traditional electronic hardware, e.g., by bioware in molecular computing. In molecular computing, data are encoded as biomolecules and then molecular biology tools are used to transform the data, thus performing computations. In quantum computing, one exploits quantum-mechanical phenomena to perform computations and secure communications more efficiently than classical physics and, hence, traditional hardware allows. The second strand of research covered by the handbook, computation taking place in nature, is represented by investigations into, among others, the computational nature of self-assembly, which lies at the core of nanoscience, the computational nature of developmental processes, the computational nature of biochemical reactions, the computational nature of bacterial communication, the computational nature of brain processes, and the systems biology approach to bionetworks where cellular processes are treated in terms of communication and interaction, and, hence, in terms of computation. We are now witnessing exciting interaction between computer science and the natural sciences. While the natural sciences are rapidly absorbing notions, techniques and methodologies intrinsic to information processing, computer science is adapting and extending its traditional notion of computation, and computational techniques, to account for computation taking place in nature around us. Natural Computing is an important catalyst for this two-way interaction, and this handbook is a major record of this important development.
Download or read book Neural Networks and Animal Behavior written by Magnus Enquist and published by Princeton University Press. This book was released on 2005-09-04 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can we make better sense of animal behavior by using what we know about the brain? This is the first book that attempts to answer this important question by applying neural network theory. Scientists create Artificial Neural Networks (ANNs) to make models of the brain. These networks mimic the architecture of a nervous system by connecting elementary neuron-like units into networks in which they stimulate or inhibit each other's activity in much the same way neurons do. This book shows how scientists can employ ANNs to analyze animal behavior, explore the general principles of the nervous systems, and test potential generalizations among species. The authors focus on simple neural networks to show how ANNs can be investigated by math and by computers. They demonstrate intuitive concepts that make the operation of neural networks more accessible to nonspecialists. The first chapter introduces various approaches to animal behavior and provides an informal introduction to neural networks, their history, and their potential advantages. The second chapter reviews artificial neural networks, including biological foundations, techniques, and applications. The following three chapters apply neural networks to such topics as learning and development, classical instrumental condition, and the role of genes in building brain networks. The book concludes by comparing neural networks to other approaches. It will appeal to students of animal behavior in many disciplines. It will also interest neurobiologists, cognitive scientists, and those from other fields who wish to learn more about animal behavior.
Download or read book Artificial Neural Networks in Biological and Environmental Analysis written by Grady Hanrahan and published by CRC Press. This book was released on 2011-01-18 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originating from models of biological neural systems, artificial neural networks (ANN) are the cornerstones of artificial intelligence research. Catalyzed by the upsurge in computational power and availability, and made widely accessible with the co-evolution of software, algorithms, and methodologies, artificial neural networks have had a profound
Download or read book Theoretical Mechanics of Biological Neural Networks written by Ronald J. MacGregor and published by Elsevier. This book was released on 2012-12-02 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical Mechanics of Biological Neural Networks presents an extensive and coherent discusson and formulation of the generation and integration of neuroelectric signals in single neurons. The approach relates computer simulation programs for neurons of arbitrary complexity to fundamental gating processes of transmembrance ionic fluxes of synapses of excitable membranes. Listings of representative computer programs simulating arbitrary neurons, and local and composite neural networks are included. - Develops a theory of dynamic similarity for characterising the firing rate sensitivites of neurons in terms of their characteristic anatomical and physiological parameters - Presents the sequential configuration theory - a theoretical presentation of coordinated firing patterns in entire neural population - Presents the outlines of mechanics for multiple interacting networks in composite systems
Download or read book The NeuroProcessor written by Yevgeny Perelman and published by Springer Science & Business Media. This book was released on 2008-08-20 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding brain structure and principles of operation is one of the major challengesofmodernscience.SincetheexperimentsbyGalvanionfrogmuscle contraction in 1792, it is known that electrical impulses lie at the core of the brain activity. The technology of neuro-electronic interfacing, besides its importance for neurophysiological research, has also clinical potential, so called neuropr- thetics. Sensory prostheses are intended to feed sensory data into patient’s brain by means of neurostimulation. Cochlear prostheses [1] are one example of sensory prostheses that are already used in patients. Retinal prostheses are currently under research [2]. Recent neurophysiological experiments [3, 4] show that brain signals recorded from motor cortex carry information regarding the movement of subject’s limbs (Fig. 1.1). These signals can be further used to control ext- nal machines [4] that will replace missing limbs, opening the ?eld of motor prosthetics, devices that will restore lost limbs or limb control. Fig. 1.1. Robotic arm controlled by monkey motor cortex signals. MotorLab, U- versity of Pittsburgh. Prof Andy Schwartz, U. Pitt 2 1 Introduction Another group of prostheses would provide treatment for brain diseases, such as prevention of epileptic seizure or the control of tremor associated with Parkinson disease [5]. Brain implants for treatment of Epilepsy and Parkinson symptoms (Fig. 1.2) are already available commercially [6, 7]. Fig. 1.2. Implantable device for Epilepsy seizures treatment [7]. Cyberonics, Inc.
Download or read book The Self Assembling Brain written by Peter Robin Hiesinger and published by Princeton University Press. This book was released on 2022-12-13 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: "In this book, Peter Robin Hiesinger explores historical and contemporary attempts to understand the information needed to make biological and artificial neural networks. Developmental neurobiologists and computer scientists with an interest in artificial intelligence - driven by the promise and resources of biomedical research on the one hand, and by the promise and advances of computer technology on the other - are trying to understand the fundamental principles that guide the generation of an intelligent system. Yet, though researchers in these disciplines share a common interest, their perspectives and approaches are often quite different. The book makes the case that "the information problem" underlies both fields, driving the questions that are driving forward the frontiers, and aims to encourage cross-disciplinary communication and understanding, to help both fields make progress. The questions that challenge researchers in these fields include the following. How does genetic information unfold during the years-long process of human brain development, and can this be a short-cut to create human-level artificial intelligence? Is the biological brain just messy hardware that can be improved upon by running learning algorithms in computers? Can artificial intelligence bypass evolutionary programming of "grown" networks? These questions are tightly linked, and answering them requires an understanding of how information unfolds algorithmically to generate functional neural networks. Via a series of closely linked "discussions" (fictional dialogues between researchers in different disciplines) and pedagogical "seminars," the author explores the different challenges facing researchers working on neural networks, their different perspectives and approaches, as well as the common ground and understanding to be found amongst those sharing an interest in the development of biological brains and artificial intelligent systems"--
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.
Download or read book Advanced Models of Neural Networks written by Gerasimos G. Rigatos and published by Springer. This book was released on 2014-08-27 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.
Download or read book Artificial Neural Networks written by David J. Livingstone and published by Humana Press. This book was released on 2011-10-09 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book, international experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. Methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology are included. This book is an excellent guide to this exciting field.
Download or read book 23 Problems in Systems Neuroscience written by Jan Leonard Hemmen and published by Oxford University Press. This book was released on 2006 with total page 531 pages. Available in PDF, EPUB and Kindle. Book excerpt: The complexity of the brain and the protean nature of behavior remain the most elusive area of science, but also the most important. van Hemmen and Sejnowski invited 23 experts from the many areas--from evolution to qualia--of systems neuroscience to formulate one problem each. Although each chapter was written independently and can be read separately, together they provide a useful roadmap to the field of systems neuroscience and will serve as a source of inspirations for future explorers of the brain.
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).
Download or read book Artificial Neural Networks written by Kevin L. Priddy and published by SPIE Press. This book was released on 2005 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical details are included in the appendices for interested readers), and with a maximum of hands-on experience. All specialized terms are included in a glossary. The result is a highly readable text that will teach the engineer the guiding principles necessary to use and apply artificial neural networks.
Download or read book Multivariate Statistical Machine Learning Methods for Genomic Prediction written by Osval Antonio Montesinos López and published by Springer Nature. This book was released on 2022-02-14 with total page 707 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.
Download or read book Artificial Neural Networks in Medicine and Biology written by H. Malmgren and published by Springer Science & Business Media. This book was released on 2000-04-12 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume comprises a selection of papers presented at ANNIMAB-1, the first conference to focus specifically on the topics of ANNs in medicine and biology. It covers three main areas: The medical applications of ANNs, such as in diagnosis and outcome prediction, medical image analysis, and medical signal processing; The uses of ANNs in biology outside clinical medicine, such as in data analysis, in molecular biology, and in simulations of biological systems; The theoretical aspects of ANNs, examining recent developments in learning algorithms and the possible role of ANNs in the medical decision process. Summarising the state-of-the-art and analysing the relationship between ANN techniques and other available methods, it also points to possible future biological and medical uses of ANNs. Essential reading for all neural network theorists, it will also be of interest to biologists and physicians with an interest in modelling and advanced statistical techniques.
