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5 produkter
5 produkter
Häftad, Engelska, 1991
556 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.
Häftad, Engelska, 1992
540 kr
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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.
Häftad, Engelska, 1992
556 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.
Häftad, Engelska, 1992
556 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.
Häftad, Engelska, 1993
1 105 kr
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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.