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检索条件"任意字段=Conference on Neural Network and Distributed Processing"

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Mapping neural network models onto transputers

Mapping neural network models onto transputers

引用

International Joint conference on neural networks (IJCNN)

作者： M.E.A. Barac Dept. of Comput Sci. Univ. Coll. London UK

Summary form only given, as follows. neural network models are reviewed from the point of view of parallel distributed processing models. More specifically, the interactions between neural models and the computational model implemented by transputer-based machines are identified so as to define a general strategy for mapping neural networks onto transputers, and therefore achieving the design and a prototype of a neural network mapper for transputer-based machines.< >

关键词： neural networks Computational modeling Computer science Educational institutions distributed processing Computer networks Prototypes

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A neural network based postattack damage assessment system

A neural network based postattack damage assessment system

引用

IEEE National conference on Aerospace and Electronics (NAECON)

作者： P. Wang L. Menegozzi ITT Avionics Nutley NJ USA

Key elements of an automated damage assessment (ADA) will include ground-based sensors to survey and measure postattack damages, communication networks to link sensors, a survival recovery center (SRC), a runway repair team (or robots) for rapid response, and advanced signal processors to perform the 'search and optimization' processes for the 'best' airbase recovery plan. To meet the USAF ADA requirements, ITT Avionics has proposed the development of a hybrid signal processor. The system will consist of algorithmic processors and neural networks. To improve DA performance, key DA functions are implemented by neural networks. Due to the intrinsic nature of distributed processing power, the neural network not only provides the high throughput required for DA but it also achieves fault tolerance and graceful degradation, which are extremely important for the Rapid Runway Repair program.< >

关键词： neural networks Signal processing Communication networks Robot sensing systems Robotics and automation Performance evaluation Aerospace electronics Signal processing algorithms distributed processing Throughput

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The parallel processing of neural networks on ring-shaped multiprocessor

The parallel processing of neural networks on ring-shaped mu...

引用

IEEE Pacific Rim conference on Communications, Computers and Signal processing

作者： H. Midorikawa Department of Information Sciences Seikei University Musashino Tokyo Japan

An efficient parallel simulation algorithm is described for a multilayered neural network with a backpropagation learning procedure on a distributed-memory multiprocessor with ring connection. Each layer is divided into P disjoint blocks and each block is mapped on each processor of a P-processor system. The parallel simulation using this algorithm has been implemented on a system of transputers with ring connection, and the time complexity of both calculation and communication is analyzed. It is shown that the algorithm can provide high efficiency on a multiprocessor system which has a large number of processors connected in a ring.< >

关键词： Parallel processing neural networks Neurons Multi-layer neural network Backpropagation Optical devices Large scale integration Electronic mail Analytical models Algorithm design and analysis

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Training of a three-layer dynamical recurrent neural network for nonlinear input-output mapping

Training of a three-layer dynamical recurrent neural network...

引用

International Joint conference on neural networks (IJCNN)

作者： S.I. Sudharsanan M.K. Sundareshan Department of Electrical and Computer Engineering University of Arizona Tucson Tucson AZ USA

A three-layer dynamical neural network with feedback and recurrent connections is proposed for nonlinear input-output mapping applications. A simple-to-implement distributed learning scheme is developed, and convergence properties of the training procedure are established. Application of the network architecture and the learning scheme to the identification of the dynamics of a nonlinear system is made, and a performance evaluation is given.< >

关键词： Recurrent neural networks neural networks Neurofeedback Multi-layer neural network Backpropagation Application software Convergence Nonlinear dynamical systems Nonlinear systems Signal processing algorithms

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processing spatio-temporal patterns by mapping time into intensity

Processing spatio-temporal patterns by mapping time into int...

引用

International Joint conference on neural networks (IJCNN)

作者： W. Banzhaf Central Research Laboratory Mitsubishi Electric Corporation Limited Amagasaki Japan

The author proposes a novel hybrid combination of networks for the processing of spatio-temporal patterns. While the instantaneous spatial components of patterns are translated into firing intensities of 'grandmother' cells, the temporal sequence of patterns is mapped into intensities of so-called TIM (time-intensity-mapping) cells. The TIM cells generate a corresponding (spatially distributed) activity pattern which in turn is processed by another set of grandmother cells. The feasibility of the approach is demonstrated by using temporal sequences of letters. The robustness against distortions of transient pattern sequences is remarkable and may lead to applications of the TIM network in speech recognition.< >

关键词： Sampling methods Information processing Laboratories Speech processing neural networks Pattern recognition Spatiotemporal phenomena Matched filters Pattern matching

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A two-stage neural network for translation, rotation and size-invariant visual pattern recognition

A two-stage neural network for translation, rotation and siz...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： A. Ravichandran B. Yegnanarayana Department of Computer Science and Engineering Indian Institute of Technology Chennai India

A two-stage neural network is described for transformation-invariant visual pattern recognition. In the first stage, features are extracted after normalizing the image. It is shown how parameters of spatial transformation can be estimated even in the presence of noise by using knowledge about rigid objects. Circular arcs in the normalized image are used as generalized features to describe the input pattern. Each image pixel contributes to the features which it can constitute. Contributions from noisy pixels are distributed over the feature space, whereas meaningful parts contribute to clusters that correspond to features of the image. In the second stage, the image is classified on the basis of these features by a multilayer perceptron network trained using a backpropagation algorithm.< >

关键词： neural networks Pattern recognition Sensor arrays Pattern matching Feature extraction Working environment noise Image sensors Image recognition Pixel Multilayer perceptrons

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Parallel simulations of Hopfield neural network on distributed-memory multiprocessors

Parallel simulations of Hopfield neural network on distribut...

引用

International Joint conference on neural networks (IJCNN)

作者： S.B. Eun S.R. Maeng H. Yoon Department of Computer Science and Center of Artificial Intelligence Research Korea Advanced Institute of Science and Technology South Korea

The authors investigate the parallel simulation of the Hopfield model on a digital computer with a multiprocessor. The synchronous and parallel mode of simulation may result in the oscillation of the network, so they suggest a serial update and parallel evaluation policy, which results in the speedup being proportional to the number of processors used while the convergence of the network is guaranteed. A mapping to a message passing multiple-instruction/multiple-data (MIMD) multiprocessor to reduce the computation time is proposed, and two updating sequences of neurons in the multiprocessor are compared and analyzed.< >

关键词： Hopfield neural networks Neurons Computational modeling Convergence Computer simulation Parallel processing Circuit simulation Computer science Artificial intelligence Electronic mail

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A multiprocessor machine for large-scale neural network simulation

A multiprocessor machine for large-scale neural network simu...

引用

International Joint conference on neural networks (IJCNN)

作者： J.G. Elias M.D. Fisher C.M. Monemi Electrical Engineering Department University of Delaware Newark DE USA Electronic Imaging Department E. l. Du Pont de Nemours and Company Inc. Newark DE USA

The architecture of a multiprocessor machine designed specifically for simulating large digital neural networks is described. The single-input multiple data (SIMD) machine comprises a host computer for high-level control and software development, a network controller which has data distribution and control responsibilities, and multiple processor arrays which carry out most of the computation for the network. Each processor array comprises a high-performance reduced instruction set computer (RISC) as a controller and 20 processing elements, each of which consist of a custom VLSI floating point processor and 1.5 Mbytes of private high-speed memory. The peak processing rate for a single processor array is 500 MFLOPS which can be sustained for relatively long vectors.< >

关键词： Large-scale systems neural networks Computational modeling Computer networks distributed computing Computer architecture Programming Computer aided instruction Reduced instruction set computing Process control

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A novel massively associative processing architecture for the implementation of artificial neural networks

A novel massively associative processing architecture for th...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： A. Krikelis Aspex MicroSystems Limited Brunel University Uxbridge UK

The author presents the ASP (associative string processor) a massively parallel, programmable, fault-tolerant architecture, which can efficiently support low-MIMD/high-SIMD and other parallel computation paradigms by providing comprehensive cover of both numeric and symbolic processing, supporting content-addressing, and a dynamically reconfigurable rich-in-connectivity interprocessor communication network. The efficient implementation of a neural network model (i.e. the backpropagation model) on the ASP is addressed and its performance, at a computational rate of 10/sup 9/ interconnections per second (i.e. 10/sup 9/ Giga-interconnections/s), is reported.< >

关键词： Associative processing Application specific processors Computer architecture Concurrent computing Computer networks Parallel processing Communication networks Analog computers High performance computing distributed computing

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Programming the harmonium

Programming the harmonium

引用

International Joint conference on neural networks (IJCNN)

作者： P. Myllymaki P. Orponen Department of Computer Science University of Helsinki Helsinki Finland

The authors show how to synthesize, given a Bayesian network description of a probability distribution, an instance of P. Smolensky's harmony network (1986) that computes maximum-likelihood completions to partial value assignments, according to the given distribution. As an application, they present a scheme for translating a high-level description of a conceptual hierarchy, with default values and exceptions, to a harmony network representation, which is then inherently capable of quite complicated query processing. Finally, the authors discuss their experience with implementing such a translation scheme.< >

关键词： Bayesian methods Markov random fields Computer networks Stochastic processes Virtual colonoscopy Knowledge representation Computer science network synthesis Probability distribution distributed computing

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