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.
This book offers the first detailed, comprehensible scientific presentation of Confabulation Theory, addressing a pressing scientific question: How does brain information processing, or cognition, work? With only elementary mathematics as a prerequisite, this book will prove accessible to technologists, scientists, and the educated public.
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.
Fuzzy sets were introduced by Zadeh (1965) as a means of representing and manipulating data that was not precise, but rather fuzzy. Fuzzy logic pro vides an inference morphology that enables approximate human reasoning capabilities to be applied to knowledge-based systems. The theory of fuzzy logic provides a mathematical strength to capture the uncertainties associ ated with human cognitive processes, such as thinking and reasoning. The conventional approaches to knowledge representation lack the means for rep resentating the meaning of fuzzy concepts. As a consequence, the approaches based on first order logic and classical probablity theory do not provide an appropriate conceptual framework for dealing with the representation of com monsense knowledge, since such knowledge is by its nature both lexically imprecise and noncategorical. The developement of fuzzy logic was motivated in large measure by the need for a conceptual framework which can address the issue of uncertainty and lexical imprecision. Some of the essential characteristics of fuzzy logic relate to the following [242]. • In fuzzy logic, exact reasoning is viewed as a limiting case of ap proximate reasoning. • In fuzzy logic, everything is a matter of degree. • In fuzzy logic, knowledge is interpreted a collection of elastic or, equivalently, fuzzy constraint on a collection of variables. • Inference is viewed as a process of propagation of elastic con straints. • Any logical system can be fuzzified. There are two main characteristics of fuzzy systems that give them better performance für specific applications.
Originally published in 1992, this work compliments and extends the theory and results of nonlinear psychophysics – an original approach created by the author. It breaks with the traditional mathematics used in the experimental psychology of sensation and draws on what is popularly known as chaos theory and its extension into neural networks. Topical and innovative in its approach, it integrates a diversity of topics previously treated separately into one framework. The properties of the mathematics used are illustrated in the context of substantive problems in psychophysics; thus, it builds strong new bridges between the dynamics of mass action in psychophysical processes and the broader phenomena of sensation. No other treatments of the topic take quite this approach; the use of systems theory, rather than traditional equations of psychophysics dating from the mid-nineteenth century, offers a striking contrast in both theory construction and data analysis.
Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.
Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.
One hundred years ago, the fundamental building block of the central nervous system, the neuron, was discovered. This study focuses on the existing mathematical models of neurons and their interactions, the simulation of which has been one of the biggest challenges facing modern science." "More than fifty years ago, W. S. McCulloch and W. Pitts devised their model for the neuron, John von Neumann seemed to sense the possibilities for the development of intelligent systems, and Frank Rosenblatt came up with a functioning network of neurons. Despite these advances, the subject had begun to fade as a major research area until John Hopfield arrived on the scene. Drawing an analogy between neural networks and the Ising spin models of ferromagnetism, Hopfield was able to introduce a "computational energy" that would decline toward stable minima under the operation of the system of neurodynamics devised by Roy Glauber." "Like a switch, a neuron is said to be either "on" or "off." The state of the neuron is determined by the states of the other neurons and the connections between them, and the connections are assumed to be reciprocal - that is, neuron number one influences neuron number two exactly as strongly as neuron number two influences neuron number one. According to the Glauber dynamics, the states of the neurons are updated in a random serial way until an equilibrium is reached. An energy function can be associated with each state, and equilibrium corresponds to a minimum of this energy. It follows from Hopfield's assumption of reciprocity that an equilibrium will always be reached." "D. H. Ackley, G. E. Hinton, and T. J. Sejnowski modified the Hopfield network by introducing the simulated annealing algorithm to search out the deepest minima. This is accomplished by - loosely speaking - shaking the machine. The violence of the shaking is controlled by a parameter called temperature, producing the Boltzmann machine - a name designed to emphasize the connection to the statistical physics of Ising spin models." "The Boltzmann machine reduces to the Hopfield model in the special case where the temperature goes to zero. The resulting network, under the Glauber dynamics, produces a homogeneous, irreducible, aperiodic Markov chain as it wanders through state space. The entire theory of Markov chains becomes applicable to the Boltzmann machine." "With ten chapters, five appendices, a list of references, and an index, this study should serve as an introduction to the field of neural networks and its application, and is suitable for an introductory graduate course or an advanced undergraduate course."--BOOK JACKET.Title Summary field provided by Blackwell North America, Inc. All Rights Reserved
The polar regions, perhaps more than any other places on Earth, give the geophysical scientist a sense of exploration. This sensibility is genuine, for not only is high-latitude ?eldwork arduous with many locations seldom or never visited, but there remains much fundamental knowledge yet to be discovered about how the polar regions interact with the global climate system. The range of opportunities for new discovery becomes strikingly clear when we realize that the high latitudes are not one region but are really two vastly di?erent worlds. The high Arctic is a frozen ocean surrounded by land, and is home to fragile ecosystems and unique modes of human habitation. The Antarctic is a frozen continent without regular human habitation, covered by ice sheets taller than many mountain ranges and surrounded by the Earth’s most forbidding ocean. When we consider global change as applied to the Arctic, we discuss impacts to a region whose surface and lower atmospheric temperatures are near the triple point of water throughout much of the year. The most consistent signatures of climate warming have occurred at northern high latitudes (IPCC, 2001), and the potential impacts of a few degrees increase in surface temperature include a reduction in sea ice extent, a positive feedback to climate warming due to lowering of surface albedo, and changes to surface runo? that might a?ect the Arctic Ocean’s salinity and circulation.
This text applies engineering science and technology to biological cells and tissues that are electrically conducting and excitable. It describes the theory and a wide range of applications in both electric and magnetic fields.
The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical guide to the application of artificial neural networks. Geared toward the practitioner, Pattern Recognition with Neural Networks in C++ covers pattern classification and neural network approaches within the same framework. Through the book's presentation of underlying theory and numerous practical examples, readers gain an understanding that will allow them to make judicious design choices rendering neural application predictable and effective. The book provides an intuitive explanation of each method for each network paradigm. This discussion is supported by a rigorous mathematical approach where necessary. C++ has emerged as a rich and descriptive means by which concepts, models, or algorithms can be precisely described. For many of the neural network models discussed, C++ programs are presented for the actual implementation. Pictorial diagrams and in-depth discussions explain each topic. Necessary derivative steps for the mathematical models are included so that readers can incorporate new ideas into their programs as the field advances with new developments. For each approach, the authors clearly state the known theoretical results, the known tendencies of the approach, and their recommendations for getting the best results from the method. The material covered in the book is accessible to working engineers with little or no explicit background in neural networks. However, the material is presented in sufficient depth so that those with prior knowledge will find this book beneficial. Pattern Recognition with Neural Networks in C++ is also suitable for courses in neural networks at an advanced undergraduate or graduate level. This book is valuable for academic as well as practical research.
Although neural modeling has a long history, most of the texts available on the subject are quite limited in scope, dealing primarily with the simulation of large-scale biological neural networks applicable to describing brain function. Introduction to Dynamic Modeling of Neuro-Sensory Systems presents the mathematical tools and methods that can de
This book introduces a host of connectionist models of cognition and behavior. The major areas covered are high-level cognition, language, categorization and visual perception, and sensory and attentional processing. All of the articles cover unpublished research work. The key contribution of this book is that it focuses exclusively on the advances in connectionist modeling in psychology. The papers are relatively short, and were explicitly written to be accessible to both connectionist modelers and experimental psychologists.
This is a self-contained introduction to the theory of information and coding. It can be used either for self-study or as the basis for a course at either the graduate or ,undergraduate level. The text includes dozens of worked examples and several hundred problems for solution.
Brain Dynamics and the Striatal Complex, the first volume in the Conceptual Advances in Brain Research book series, relates dynamic function to cellular structure and synaptic organization in the basal ganglia. The striatum is the largest nucleus within the basal ganglia and therefore plays an important role in understanding structure/function relationships. Areas covered include dopaminergic input to the striatum, organization of the striatum, and the interaction between the striatum and the cerebral cortex.
The two volumes LNAI 2773 and LNAI 2774 constitute the refereed proceedings of the 7th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2003, held in Oxford, UK in September 2003. The 390 revised papers and poster papers presented were carefully reviewed and selected from numerous submissions. Among the areas covered are knowledge-based systems, neural computing, fuzzy logic, uncertainty, machine learning, soft computing, agent systems, intelligent agents, data mining, knowledge discovery, hybrid intelligent systems, natural language processing, information retrieval, Web applications, case-based reasoning, evolutionary computing, signal processing, ontologies, decision making, human-computer interaction, intelligent user interfaces, neuroscience, intelligent agents, biocomputing, etc.
The three volume set LNAI 4251, LNAI 4252, and LNAI 4253 constitutes the refereed proceedings of the 10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006, held in Bournemouth, UK, in October 2006. The 480 revised papers presented were carefully reviewed and selected from about 1400 submissions. The papers present a wealth of original research results from the field of intelligent information processing.
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