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

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Recognition system by neural network for additional learning

Recognition system by neural network for additional learning

引用

Proceedings of 1993 IEEE/RSJ International conference on Intelligent Robots and Systems (IROS '93)

作者： S. Shiotani T. Fukuda T. Shibata Department of Mechano Informatics Systems University of Nagoya Nagoya Aichi Japan

The authors propose a neural network (NN) model for pattern recognition which can learn new patterns without losing patterns memorized in the past. This model is called a neural network based on distance between patterns (NDP). The NDP uses the radial basis function (RBF) as the response function in place of the sigmoid function. The response function is a smooth function similar to the Gaussian base function and the probability density function. The response function learns patterns faster than the multilayered perceptron (MLP) as well as other RBF NNs. The most salient architectural feature of the NDP is self-organization by adding nodes in the output layer one by one. The NDP also varies the curve of the response function by tuning the center and the width of the response function, and separates the input space into regions for each category appropriately.

关键词： neural networks Artificial neural networks Subspace constraints Humans Image processing Charge coupled devices distributed processing Radial basis function networks Error correction Education

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Initial Investigation of Modal Parameter Estimation for Flexible Manipulator Using Hopfield network

Initial Investigation of Modal Parameter Estimation for Flex...

引用

IEEE Regional conference on Aerospace Control Systems

作者： Hsin-Tan Chiu Ching-Fang Lin S. Cetinkunt American GNC Corporation Chatsworth CA USA Department of Mechanical Engineering University of Illinois Chicago Chicago IL USA

A type of recurrent artificial neural network (ANN) is studied for identification problems of modal parameter of linear structural systems. This recurrent ANN model is closely related to the analog Hopfield network operated in synchronous mode where the connection strengths of the ANN are determined from the system state measurements at each sampling time. The states of neurons represent the modal parameters of the linear structural system to be identified. The recurrent ANN model preserves both the parallelism and the distributed processing nature of the analog Hopfield model as well as asymptotically improving the least squares estimation error. Therefore, it is a good candidate for use in real-time control as a parametric estimator, in particular, for large dimensional distributed parameter systems. Simulation results for the estimation of modal parameters of a flexible beam are presented to determine the effectiveness of this ANN architecture.

关键词： Parameter estimation Artificial neural networks Neurons neural networks Feedforward neural networks Associative memory Manipulators Mechanical engineering Time measurement Sampling methods

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Optimal parallel back-propagation schemes for mesh-connected and bus-connected multiprocessors

Optimal parallel back-propagation schemes for mesh-connected...

引用

International conference on neural networks

作者： T. Yukawa T. Ishikawa NTT Network Information Systems Laboratories Musashino Tokyo Japan

Optimal parallel schemes that minimize communication overhead for the backpropagation algorithm on neural networks (NNs) are proposed. A parallel computation framework of the backpropagation algorithm is discussed, and the lower-bound of communication overhead is provided. Optimal schemes for mesh-connected and bus-connected architectures based on this framework are proposed. The scheme for the mesh-connected architecture achieves the lower-bound of communication overhead. The scheme for the bus-connected architecture reduces it to the square-root order of the number of processors, while the conventional scheme requires a linear order overhead.< >

关键词： Concurrent computing Head neural networks distributed computing Multi-layer neural network Partitioning algorithms Information systems Electronic mail Pattern classification Image processing

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HF radio network simulation based on automatic link establishment (ALE) and distributed routing policies

HF radio network simulation based on automatic link establis...

引用

MILCOM, Military Communications conference

作者： J. Cleveland C3 Systems Division Electrospace Systems Inc. TX USA

The application of the automatic link establishment (ALE) procedures of MIL-STD-188-141A, combined with improved signal processing technology found in advanced modems, provides the basic elements to create adaptive high frequency (HF) radio networks. Dynamic changes in ionospheric conditions and noise fluctuations make the HF network a hostile one, where links between nodes are subject to frequent failure. The addition of adaptive routing and information management enhances network robustness in this environment. This suggests the use of a distributed routing algorithm in which convergence to the shortest path is of primary concern. However, routing decisions derived from such distributed processes exhibit an inherent uncertainty due to the randomness of link failures and the latency of network performance information. The authors describe predictions of a computer simulation of a wide area HF radio network that invokes ALE procedures, adaptive routing, and information management protocols. Aspects include the impact of modem interleaver delay time, encryption synchronization delay, management overhead, and message handling on throughput and network latency. Recommendations are presented with respect to the application of statistical neural processing to improve network management in this environment.< >

关键词： Hafnium Radio network Routing Modems Information management Delay effects Adaptive signal processing Frequency Working environment noise Fluctuations

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Semantic ambiguity resolution by parallel distributed, associative inference and contradiction detection

Semantic ambiguity resolution by parallel distributed, assoc...

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International Joint conference on neural networks (IJCNN)

作者： T. Tsunoda H. Tanaka Department of Electrical Engineering Faculty of Engineering University of Tokyo Tokyo Japan

Proposes a PDAI&CD architecture aimed at constructing natural inference systems. The kernel consists of a mutually associative neural network which processes numerical patterns and of a logical system processing symbols. The associative part calls on context-dependent free-association of concepts based on the relations of concepts acquired from a dynamically changing outer world. In the logical part of the architecture, the results obtained by the neural network are checked, and emerging contradictions create feedback to the associative network and thus find a final optimum solution. WAVE, an implemented system, is introduced, and the authors show its application to the problem of ambiguity resolution in natural language understanding.

关键词： neural networks Natural languages Neurofeedback Humans Laboratories Kernel Concrete Motion pictures Biological system modeling Context modeling

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Automated Learning of Workload Measures for Load Balancing on a distributed System

Automated Learning of Workload Measures for Load Balancing o...

引用

International conference on Parallel processing (ICPP)

作者： Pankaj Mehra Benjamin W. Wah University of Illinois Urbana-Champaign USA Coordinated Science Laboratory University of Illinois Urbana-Champaign Urbana IL USA

Load-balancing systems use workload indices to dynamically schedule jobs. We present a novel method of automatically learning such indices. Our approach uses comparator neural networks, one per site, which learn to predict the relative speedup of an incoming job using only the resource-utilization patterns observed prior to the job's arrival. Our load indices combine information from the key resources of contention: CPU, disk, network, and memory.

关键词： Load management Parallel processing Coordinate measuring machines neural networks Intelligent networks Degradation

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Towards a general distributed platform for learning and generalization

Towards a general distributed platform for learning and gene...

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New Zealand International Two-Stream conference on Artificial neural networks and Expert Systems

作者： T.R. Martinez B.W. Hughes Computer Science Department Brigham Young University Provo UT USA

Different learning models employ different styles of generalization on novel inputs. The need for multiple styles of generalization to support a broad application base is discussed. The priority ASOCS (PASOCS) model (priority adaptive self-organizing concurrent system) is presented as a potential platform which can support multiple generalization styles. PASOCS is an adaptive network composed of many simple computing elements operating asynchronously and in parallel. PASOCS can operate in either a data processing mode or a learning mode. During data processing mode, the system acts as a parallel hardware circuit. During learning mode, PASOCS incorporates rules, with attached priorities, which represent the application being learned. Learning is accomplished in a distributed fashion in time logarithmic in the number of rules. The new model has significant learning time and space complexity improvements over previous models.< >

关键词： Adaptive systems Concurrent computing Learning systems Data processing Circuits Logic Computer science Application software Computer networks Hardware

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A VLSI array processor for neural network algorithms

A VLSI array processor for neural network algorithms

引用

Custom Integrated Circuits conference (CICC)

作者： J. Beichter N. Bruls U. Ramacher E. Sicheneder H. Klar Corporate Research and Development Munich Germany Technical University Berlin Berlin Germany

A chip based on a new scalable parallel systolic VLSI architecture is presented for executing the compute-bound algorithmic primitives used by search and learning algorithms in neural networks and low-level signal processing. The architecture combines high performance with a high grade of flexibility for all types and sizes of neural networks. The processor chip can be connected to form 1-D and 2-D arrays. By offering an accuracy of 16 b for input and 47 b for output data, the chip achieves 800M connections/s at 50 MHz. It is realized in 1.0-/spl mu/m CMOS (610K transistors on 13.7 /spl times/ 13.7 mm/sup 2/) and has a total data bandwidth of 10.9 Gb/s.

关键词： Very large scale integration neural networks Adders Computer architecture Computer networks Silicon Statistics Parallel processing Joining processes distributed computing

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Conceptual framework for implementing neural networks on massively parallel machines

Conceptual framework for implementing neural networks on mas...

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Proceedings of the 6th International Parallel processing Symposium

作者： Azema-Barac, Magali E. Univ College London London United Kingdom

ISBN: (纸本)0818626720

This paper describes a framework for implementing neural networks on massively parallel machines. The framework is generic and applies to a range of neural networks (Multi Layer Perception, Competitive Learning, Self-Organizing Map, etc.) as well as a range of massively parallel machines (Connection Machine, distributed Array Processor, MasPar). It consists of two phases: an abstract decomposition of neural networks and a machine specific decomposition. The abstract decomposition identifies the parallelism implemented by neural networks, and provides alternative distribution schemes according to the required exploitation of parallelism. The machine specific decomposition considers the relevant machine criteria, and integrates these with the result of the abstract decomposition to form a `decision' system. This system formalizes the relative gain of each distribution scheme according to neural network and machine criteria. It then identifies their possible optimizations. Finally, it computes and ranks the absolute speed up of each distribution scheme.

关键词： Parallel processing systems

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Adaptive distributed orthogonalization processing for principal components analysis

Adaptive distributed orthogonalization processing for princi...

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1992 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 1992

作者： Chen, Hong Liu, Ruey-Wen Department of Electrical Engineering University of Notre Dame Notre DameIN46556 United States

ISBN: (纸本)0780305329

Adaptive extraction of principal components of a vector stochastic process is a topic currently receiving much attention [l]-[8]. In this paper, we propose a new learning algorithm implemented on a neural-like network. This algorithm is shown to be superior to previous ones. The convergence of this new algorithm can be proved, but only an outline of the proof is presented here. © 1992 IEEE.

关键词： Random processes

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