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31 produkter
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Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.
Neural Nets WIRN Vietri-01
Proceedings of the 12th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 17–19 May 2001
Häftad, Engelska, 2013
552 kr
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This volume contains the proceedings of the 12th Italian Workshop on Neural Nets WIRN VIETRI-Ol, jointly organized by the International Institute for Advanced Scientific Studies "Eduardo R. Caianiello" (IIASS), the Societa Italiana Reti Neuroniche (SIREN), the IEEE NNC Italian RIG and the Italian SIG of the INNS. Following the tradition of previous years, we invited three foreign scientists to the workshop, Dr. G. Indiveri and Professors A. Roy and R. Sun, who respectively presented the lectures "Computation in Neuromorphic Analog VLSI Systems", "On Connectionism and Rule Extraction", "Beyond Simple Rule Extraction: Acquiring Planning Knowledge from Neural Networks" (the last two papers being part of the special session mentioned below). In addition, a review talk was presented, dealing with a very up-to-date topic: "NeuroJuzzy Approximator based on Mamdani's Model". A large part of the book contains original contributions approved by referees as oral or poster presentations, which have been assembled for reading convenience into three sections: Architectures and Algorithms, Image and Signal Processing, and Applications. The last part of the books contains the papers of the special Session "From Synapses to Rules". Our thanks go to Prof. B. Apolloni, who organized this section. Furthermore, two sections are dedicated to the memory of two great scientists who were friends in life, Professors Mark Aizerman and Eduardo R. Caianiello. The editors would like to thank the invited speakers, the review lecturers and all the contributors whose highly qualified papers helped with the success of the workshop.
Neural Nets WIRN VIETRI-98
Proceedings of the 10th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 21–23 May 1998
Häftad, Engelska, 2011
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From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science. This volume contains selected papers from WIRN Vietri-98, the 10th Italian Workshop on Neural Nets, 21-23 May 1998, Vietri sul Mare, Salerno, Italy. This annual event, sponsored amongst others by the IEEE Neural Network Council and the INNS/SIG Italy, brings together the best of research from all over the world. The papers cover a range of key topics within neural networks, including pattern recognition, signal processing, hybrid systems, mathematical models, hardware and software design, and fuzzy techniques. It also includes two review talks on a Morpho-Functional Model to Describe Variability Found at Hippocampal Synapses and Neural Networks and Speech Processing. By providing the reader with a comprehensive overview of recent research in this area, the volume makes a valuable contribution to the Perspectives in Neural Computing Series.
Neural Nets WIRN Vietri-99
Proceedings of the 11th Italian Workshop on Neural Nets, Vietri Sul Mare, Salerno, Italy, 20–22 May 1999
Häftad, Engelska, 2012
1 064 kr
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From its early beginnings in the fifties and sixties, the field of neural networks has been steadily developing to become one of the most interdisciplinary areas of research within computer science. This volume contains a selection of papers from WIRN Vietri-99, the 11th Italian Workshop on Neural Nets. This annual event, sponsored, amongst others, by the IEEE Neural Networks Council and the INNS/SIG Italy, brings together the best of research from all over the world. The papers cover a range of topics within neural networks, including pattern recognition, signal and image processing, mathematical models, neuro-fuzzy models and economics applications.
Neural Nets WIRN VIETRI-96
Proceedings of the 8th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 23–25 May 1996
Häftad, Engelska, 2011
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This volume contains the Proceedings of the 8th Italian Workshop on Neural Nets WIRN VIETRI-96, organized by the International Institute for Advanced Scientific Studies "Eduardo R. Caianiello" and by the Societa Italiana Reti Neuroniche (SIREN). We would like to thank Professor L.B. Almeida who agreed to deliver an invited lecture which is included in these Proceedings. The spectrum of contributors and participants covers the activity of Italian research in the field and of some European groups. The highly qualified and motivated attendance is*proof of the interest with which this annual event has met in the Italian scientific community. Maria Marinaro Roberto Tagliaferri Organizing - Scientific Committee: B. Apolloni (Univ. Milano), A. Bertoni (Univ. Milano), D.o. Caviglia (Univ. Genova), P. Campadelli (Univ. Milano), M. Ceccarelli (CNR NapolD, A. Colla (ELSAG - Genova), M. Frixione (/lASS), C. Furlanello (IRST - Trento), G.M. Guazzo (!lASS), M. Gori (Univ. Firenze), F. Lauria (Univ. Napo/D, M. Marinaro (Univ. Salerno - flASS), F. Masulli (Univ. Genova), P. Morasso (Univ. Genova), G. Orlandi (Univ. Roma), E. Pasero (Politecnico Torino), A. Petrosino (CNR Napoli), M. Protasi (Univ.Roma Ilj, S. Rampone (/lASS), R. Serra (Gruppo Ferruzzi Ravenna), F. Sorbello (Univ. Palermo), R. Stefanel.1i (Politecnico Milano), R. Tagliaferri (Univ. Salerno), R. Vaccaro (CNR Napo/D.
Neural Nets WIRN VIETRI-97
Proceedings of the 9th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 22–24 May 1997
Häftad, Engelska, 2011
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This volume contains the Proceedings of the 9th Italian Workshop on Neural Nets WIRN VIETRI-97, organised by the International Institute for Advanced Scientific Studies ((Eduardo R. Caianiello" (IIASS), the Societa Italiana Reti Neuroniche (SIREN) and the IEEE NNC Italian RIG. As in the previous editions some invited and reviewed talks on updated subjects are presented in addition to the original contributions selected by the Refereeing Committee. Also included is Professor C.M. Bishop's Invited paper on: * Latent Variables, Topographic Mappings and Data Visualization; and two review talks that deal with updated topics: * Fuzzy Neural Networks for Pattern Recognition; * A Unifying View of Gradient Calculations and Learning for Locally Recurrent Neural Networks; For publication the original contributions have been assembled into 4 sections: Applications, Architectures and Algorithms, Mathematical Models, Pattern Recognition and Robotics.The Editors thank the invited speaker and all the participants for having contributed to the success of the Workshop by submitting high quality manuscripts, and also express gratitude to the Refereeing Committee for the high quality of the selection process. Maria Marinaro Roberto Tagliaferri VI Organizing -Scientific Committee: B. Apolloni (Univ. Milano), A. Bertoni (Univ. Milano), D.O. Caviglia (Univ. Genova), P. Campadelli (Univ. Milano), A. Colla (ELSAG BAILEY -Genova), M. Frixione (1IASS), C. Furlanello (IRST -Trento), A. Esposito (1IASS), G.M. Guazzo (1IASS), M. Gori (Univ. Firenze), F. Lauria (Univ. Napoli), M. Marinaro (Univ. Salerno - IIASS), F.
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Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular they show how these methods may be applied to the topics of supervised and unsupervised learning including feature extraction, linear and non-linear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines, notably cognitive scientists, engineers, physicists, statisticians, and computer scientists, will find this to be a very valuable introduction to this topic.
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The chapters in this volume consist of articles written by leading researchers in the field of Combining Artificial Neural Nets, and as such provide a unique coverage of the area. The techniques that are presented include ensemble-based, and modular approaches. The presentation of techniques is accompanied by analysis and evaluation of their relative effectiveness, and by reports of ther application to a variety of problems. The chapters make clear the effectiveness of combining in increasing performance, and the limitations of using a simple unitary net. For this reason this volume should be required reading for all those concerned with any applications of artificial neural nets where good generalisation performance is important.
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This volume provides a state-of-the-art survey of artificial neural network applications in biomedical diagnosis, laboratory data analysis and related practical areas. It looks at biomedical applications which involve customising neural network technology to resolve specific difficulties with data processing, and deals with applications relating to particular aspects of clinical practice and laboratory or medically-related analysis. Each chapter is self-contained with regard to the technology used, covering important technical points and implementation issues like the design of user interfaces and hardware/software platforms. Artificial Neural Networks in Biomedicine will be of interest to computer scientists and neural network practitioners who want to extend their knowledge of issues relevant to biomedical applications, developers of clinical computer systems, and medical researchers looking for new methods and computational tools.
Connectionist Models in Cognitive Neuroscience
The 5th Neural Computation and Psychology Workshop, Birmingham, 8–10 September 1998
Häftad, Engelska, 1999
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1. Introdudion This volume collects together the refereed versions of 25 papers presented at the 5th Neural Computation and Psychology Workshop (NCPW5), held at the University of Birmingham from the 8th until the lOth of September 1998. The NCPW is a well-established, lively forum, which brings together researchers from a range of disciplines (artificial intelligence, mathematics, cognitive science, computer science, neurobiology, philosophy and psychology), all of whom are interested in the application of neurally-inspired (connectionist) models to topics in psychology. The theme of the 5th workshop in the series was Connectionist models in cognitive neuroscience', and the workshop aimed to bring together papers focused on the inter-relations between functional (psychological) accounts of cognition and neural accounts of underlying brain processes, linked by connectionist models. From the very beginnings of modern psychology, with the work of William James and his contemporaries, researchers have believed it important to relate behavioural analyses to neurological underpinnings. However, with the advent of connectionist modelling, where models are at least inspired by neuronal processes, this enterprise has received a new boost. With this volume, we hope that this volume adds one further mosaic stone to this ambitious objective, of unifying functional and neuronal accounts of performance.
Self-Organising Neural Networks
Independent Component Analysis and Blind Source Separation
Häftad, Engelska, 1999
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This volume presents the theory and applications of self organizing neural network models which perform the independent component analysis (ICA) transformation and blind source separation (BSS). It covers the fundamental concepts of information theory, higher order statistics and in formation geometry. Neural models for instantaneous and temporal BSS and their adaptation algorithms are presented and studied in detail. There is also in-depth coverage of the following application areas: noise reduction, speech enhancement in noisy environments, image enhancement, feature extraction for classification, data analysis and visualization, data mining and biomedical data analysis.
Neural Networks for Conditional Probability Estimation
Forecasting Beyond Point Predictions
Häftad, Engelska, 1999
536 kr
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Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.
Principles of Neural Model Identification, Selection and Adequacy
With Applications to Financial Econometrics
Häftad, Engelska, 1999
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Neural networks have had considerable success in a variety of disciplines including engineering, control, and financial modelling. However a major weakness is the lack of established procedures for testing mis-specified models and the statistical significance of the various parameters which have been estimated. This is particularly important in the majority of financial applications where the data generating processes are dominantly stochastic and only partially deterministic. Based on the latest, most significant developments in estimation theory, model selection and the theory of mis-specified models, this volume develops neural networks into an advanced financial econometrics tool for non-parametric modelling. It provides the theoretical framework required, and displays the efficient use of neural networks for modelling complex financial phenomena. Unlike most other books in this area, this one treats neural networks as statistical devices for non-linear, non-parametric regression analysis.
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Independent Component Analysis (ICA) is a fast developing area of intense research interest. Following on from Self-Organising Neural Networks: Independent Component Analysis and Blind Signal Separation, this book reviews the significant developments of the past year.It covers topics such as the use of hidden Markov methods, the independence assumption, and topographic ICA, and includes tutorial chapters on Bayesian and variational approaches. It also provides the latest approaches to ICA problems, including an investigation into certain "hard problems" for the very first time.Comprising contributions from the most respected and innovative researchers in the field, this volume will be of interest to students and researchers in computer science and electrical engineering; research and development personnel in disciplines such as statistical modelling and data analysis; bio-informatic workers; and physicists and chemists requiring novel data analysis methods.
Artificial Neural Networks in Medicine and Biology
Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000
Häftad, Engelska, 2000
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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.
Connectionist Models of Learning, Development and Evolution
Proceedings of the Sixth Neural Computation and Psychology Workshop, Liège, Belgium, 16–18 September 2000
Häftad, Engelska, 2001
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Connectionist Models of Learning, Development and Evolution comprises a selection of papers presented at the Sixth Neural Computation and Psychology Workshop - the only international workshop devoted to connectionist models of psychological phenomena. With a main theme of neural network modelling in the areas of evolution, learning, and development, the papers are organized into six sections: The neural basis of cognition Development and category learning Implicit learning Social cognition Evolution Semantics Covering artificial intelligence, mathematics, psychology, neurobiology, and philosophy, it will be an invaluable reference work for researchers and students working on connectionist modelling in computer science and psychology, or in any area related to cognitive science.
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Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing.It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Neural Networks and the Financial Markets
Predicting, Combining and Portfolio Optimisation
Häftad, Engelska, 2002
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This volume looks at financial prediction from a broad range of perspectives. It covers: - the economic arguments - the practicalities of the markets - how predictions are used - how predictions are made - how predictions are turned into something usable (asset locations) It combines a discussion of standard theory with state-of-the-art material on a wide range of information processing techniques as applied to cutting-edge financial problems. All the techniques are demonstrated with real examples using actual market data, and show that it is possible to extract information from very noisy, sparse data sets. Aimed primarily at researchers in financial prediction, time series analysis and information processing, this book will also be of interest to quantitative fund managers and other professionals involved in financial prediction.
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This volume provides an overview of important work carried out by Professor Walter Freeman of the University of Berkeley, California, USA. Collecting together his published works over the last 35 years, it charts his groundbreaking research into perception and other cognitive operations in animals and humans and looks at how this can be applied to computer hardware to provide the foundations for novel - and greatly improved - machine intelligence. It provides a step-by-step description of the concepts and data needed by electrical engineers, computer scientists and cognitivists to understand and emulate pattern recognition in biological systems at a level of competence which has not yet been matched by any form of Artificial Intelligence. It offers a unique blend of theory and experiment and, historically, it also demonstrates the impact of computers on the design, execution, and interpretation of experiments in neurophysiology over the past five decades.
Theory and Applications of Neural Networks
Proceedings of the First British Neural Network Society Meeting, London
Häftad, Engelska, 1991
552 kr
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This volume contains the papers presented at the first British Neural Network Society Meeting (NMC '90) held at King's College London, on 18-20 April 1990. The meeting was sponsored by the London Mathematical Society. The papers include introductory tutorial lectures, invited, and contributed papers. The invited contributions were given by experts from the USA, Finland, Denmark, Germany and the UK. The first day was devoted to tutorials. Professor Stephen Grossberg (Boston University) was a guest speaker on the first day, giving a thorough introduction to his adaptive resonance theory of neural networks. Subsequent tutorials covered dynamical systems and neural networks, realistic neural modelling, pattern recognition using neural networks and a review of hardware for neural network simulations. The contributed papers, given on the second day, demonstrated the breadth of interests of workers in the field. They covered topics in pattern recognition, multi-layer feedforward neural networks, network dynamics, memory and learning.On the final day, talks were given by Professor Kohonen (on self organizing maps), Professor Kurten (on the dynamics of random and structured nets) and Professor Cotterill (on modelling the visual cortex). Dr. Mayes presented a paper on various models for amnesia.
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This volume contains the proceedings of a day conference on coupled neuronal oscillators (CNO '90, London), an important aspect of neural network research, which may hold the key to a clearer appreciation of the manner in which neural networks can control complex behaviour. The contributors to the conference looked at the physiological properties of neurons in terms of electrochemical features, using these to consider possible directions for future research. The papers cover a range of areas, including an introduction to non-linear oscillators, bifurcating neurons, the modelling of cardiac rhythm, gamma oscillations, association and consciousness, and a model for low threshold oscillations in neurons. The papers also include new explanations and models of the phenomena of coupled oscillatory neural assemblies, ranging from those for the multi-phasic response of single neurons, to possible models of image segmentation.
Neural Network Dynamics
Proceedings of the Workshop on Complex Dynamics in Neural Networks, June 17–21 1991 at IIASS, Vietri, Italy
Häftad, Engelska, 1992
552 kr
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"Neural Network Dynamics" is the latest volume in the "Perspectives in Neural Computing" series. It contains papers presented at the 1991 Workshop on Complex Dynamics in Neural Networks, held at IIASS in Vietri, Italy. The workshop encompassed a wide range of topics in which neural netowrks play a fundamental role, and aimed to bridge the gap between neural computation and computational neuroscience. The papers - which have been updated where necessary to include new results - are divided into four sections covering the foundations of neural network dynamics, oscillatory neural networks, scientific and biological applications. Among the topics discussed are: a general analysis of neural network activity; descriptions of various network architectures and nodes; correlated neuronal firing; a theoretical framework for analyzing the behaviour of real and simulated neuronal networks; the structural properties of proteins; nuclear phenomenology; resonance searches in high energy physics; the investigation of information storage; visual cortical architecture and visual processing.
Neural Network Applications
Proceedings of the Second British Neural Network Society Meeting (NCM91), London, October 1991
Häftad, Engelska, 1992
552 kr
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These papers reflect the wide spectrum of neural network applications that are currently being attempted in industry and medicine. They cover medical diagnosis, robotics, plant control, machine learning, and visual inspection, as well as more general discussions on net learning and knowledge representation. The breadth and depth of coverage is a sign of the health of the subject, as well as indicating the importance of neural network developments in industry and the manner in which the applications are progressing. Among the topics covered are learning algorithms - theory and practice; a review of medical diagnostic applications of neural networks; simulated ultrasound tomographic imaging of defects; linear quad-trees for neural network based position invariant pattern recognition; the pRTAM as a hardware-realizable neuron; and the cognitive modalities ("CM") system of knowledge representation.
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The neural network is one of the most exciting new tools for information processing to have emerged over the last few years. An increasing number of computer tasks require the use of human-like capabilities, a fact which has caused severe problems for many industries and services in the past. Now, however, neural networks are being developed which can handle a broad range of tasks in vision, speed recognition, time series analysis, robotics and control. They have proved particularly useful in dealing with problems which cannot be solved by explicit rules, which involve noisy data, and which allow a system to be trained by examples. In response to increasing neural network activity during the 1980s, the DEANNA Project (Database for European Artificial Neural Network Activity) was established in 1990 (ESPRIT Project P5668). The main objective of this 15-month EC-funded project was to construct a database of current European neural computing research which could be used to assess the success of various applications, and to pinpoint the major difficulties in technology transfer.The project also aimed to create a European neural network advisory structure, and to develop neural computing simulators and demonstrators. This text contains a comprehensive overview of neural networks based on the findings of the DEANNA Project, together with a view of the broader framework of the subject in a world context. The first part of the report describes neural nets and their available hardware and software simulators. The second part discusses deeper principles, and reviews a range of applications. The final section describes the results of the DEANNA questionnaire, and provides a summary of developments in current European neural network activity.
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This text examines the design of an automated system for financial time series forecasting. It explores the level of automation which can be achieved by a system for modelling a given financial time series with the minimum of human intervention. It aims to help the reader understand the issues involved in setting neural network, or genetic algorithm parameters, and to develop methods to deal with the problems they raise in a practical manner.
Second-Order Methods for Neural Networks
Fast and Reliable Training Methods for Multi-Layer Perceptrons
Häftad, Engelska, 1997
552 kr
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This volume aims to develop understanding of theoretical and practical issues involved in the development of efficient MLP training strategies, and to describe and evaluate the performance of a wide range of specific training algorithm. Particular emphasis is given to the development of methods which have a strong theoretical foundation, rather than heuristic, "rule of thumb" training strategies. "Second order methods for neural networks" should be of interest to academic researchers and postgraduate students working with neural networks (especially supervised learning with multi-layer perceptrons), industrial researchers and programmers developing neural network software, and professionals using neural networks as optimisation tools.
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This volume brings together a collection of top international researchers in the field of artificial neural networks with the = common theme being an attempt to tackle the problem of complexity. The contributions range from more theoretical analyses of the neural network approach to a number of application-oriented articles which indicate the extent of the problem from a more practical viewpoint. The use of neural networks is a relatively new, but increasingly popular, approach to the problem of complexity. Dealing with Complexity is an extremely multi-disciplinary = examination of the above issues: although primarily intended for industrial/academic researchers, and postgraduate students working within computing science, it will also be of interest to anyone=20 working on relevant research projects or applications within the following fields: physics, mathematics and engineering.
536 kr
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Evolutionary Learning Algorithms for Neural Adaptive Control is an advanced textbook, which investigates how neural networks and genetic algorithms can be applied to difficult adaptive control problems which conventional results are either unable to solve , or for which they can not provide satisfactory results. It focuses on the principles involved, rather than on the modelling of the applications themselves, and therefore provides the reader with a good introduction to the fundamental issues involved.
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Concepts for Neural Networks - A Survey provides a wide-ranging survey of concepts relating to the study of neural networks. It includes chapters explaining the basics of both artificial neural networks and the mathematics of neural networks, as well as chapters covering the more philosophical background to the topic and consciousness. There is also significant emphasis on the practical use of the techniques described in the area of robotics. Containing contributions from some of the world's leading specialists in their fields (including Dr. Ton Coolen and Professor Igor Aleksander), this volume will provide the reader with a good, general introduction to the basic concepts needed to understan d and use neural network technology.
4th Neural Computation and Psychology Workshop, London, 9–11 April 1997
Connectionist Representations
Häftad, Engelska, 1997
521 kr
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This volume contains a collection of papers presented at the 4th Neural Computation and Psychology Workshop, held in London from 7-11 April 1997. The theme of the workshop was: Connectionist Representations: Theory and Practice which covers many importan t issues ranging from the philosophical (such as the grounding problem), and the physiological (what can connectionist representations tell us about the real neural system?), to the technical (what is needed in order to get specific models to work?). The topic is one of increasing importance within Neural Computing and covers issues of interest to researchers from a wide range of backgrounds including: artificial intelligence, applied mathematics, cognitive science, computer science, neurobiology, phi losophy and psychology. In providing a comprehensive overview of this topic it provides an invaluable contribution to the Perspectives in Neural Computing series.