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A parallel distributed processing technique for job-shop scheduling problems

A parallel distributed processing technique for job-shop sch...

引用

International Joint conference on neural networks (IJCNN)

作者： Chun-Chi Lo Ching-Chi Hsu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)0780314212

This paper presents a new parallel distributed processing (PDP) approach to solve job-shop scheduling problem which is NP-complete. In this approach, a stochastic model and a controlled external energy is used to improve the scheduling solution iteratively. Different to the processing element (PE) of the Hopfield neural network model, each PE of our model represents an operation of a certain job. So, the functions of each PE are a little more complicated than that of a Hopfield PE. Under such model, each PE is designed to perform some stochastic, collective computations. From the experimental result, the solutions can be improved toward optimal ones much faster than other methods. Instead of the polynomial number of variables needed in neural network approach, the variables number needed to formulate a job-shop problem in our model is only a linear function of the operation number contained in the given job-shop problem.

关键词： distributed processing Optimal scheduling Processor scheduling Stochastic processes Hopfield neural networks neural networks Automatic control Computer science Power engineering and energy Polynomials

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Color opponency as the internal representation acquired by a three-layered neural network model

Color opponency as the internal representation acquired by a...

引用

International conference on neural networks

作者： S. Usui S. Nakauchi Department of Information & Computer Sciences Toyohashi University of Technology Toyohashi Japan

The color representation in the visual system is discussed through the analysis of a three-layered neural network model incorporating physiological evidence of color representation at the sensor level and the perceptual level. The model is trained to perform a mapping between these color representations by a backpropagation algorithm. The acquired characteristics of the hidden units are analyzed. The hidden units learn characteristics similar to those of the color opponent cells found in fish retina and macaque lateral genticulate nucleus (LGN). It is concluded that the R-G and Y-B color opponent representations play an essential role in color information processing by investigating the efficiency of color representation in the hidden layer and the capability of color discrimination task of the model.< >

关键词： neural networks Color Visual system Computer networks Narrowband Retina Sensor systems Concurrent computing distributed computing distributed processing

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Problems of massive parallelism in neural network simulation

Problems of massive parallelism in neural network simulation

引用

International conference on neural networks

作者： A. Zell N. Mache M. Vogt M. Huttel Institute for Parallel and Distributed High Performance Systems (IPVR) University of Stuttgart Stuttgart Germany Inst. for Parallel & Distributed High Performance Syst. Stuttgart Univ. Germany

Different massively parallel implementations of multilayer feedforward neural networks are presented and compared on a MasPar MP-1216, a parallel single instruction, multiple data (SIMD) computer with 16384 processors. For multilayer feedforward networks, sustained rates of up to 348 M CPS and 129 M CUPS with backpropagation are obtained, a high mark for general purpose SIMD computers. Emphasis is placed on the problems of mapping neural networks to parallel hardware, on implementation problems in obtaining high propagation rates on a SIMD machine, and on problems with the resulting learning algorithms.< >

关键词： neural networks Multi-layer neural network Computer networks Parallel processing Computational modeling Feedforward neural networks Computer aided instruction Concurrent computing Backpropagation neural network hardware

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On neural networks for symbolic processing

On neural networks for symbolic processing

引用

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

作者： R. Sun Department of Computer Science University of Alabama Tuscaloosa AL USA

Existing approaches to integrating neural and symbolic processing are divided into the following four categories: developing specialized, structured, localist networks for symbolic processing; performing symbolic processing in distributed neural networks (in a holistic way); combining separate symbolic and neural network modules; and using neural networks as basic elements in symbolic architectures (the embedded approach). Research issues that need to be addressed in order to advance this field as well as to better understand the nature of intelligence and cognition are outlined.< >

关键词： neural networks Artificial neural networks Artificial intelligence Information processing Intelligent networks Cognition Power system modeling Sun Computer science Associative memory

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Assigning ADT modules with random neural networks

Assigning ADT modules with random neural networks

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Annual Hawaii International conference on System Sciences (HICSS)

作者： L.R. Welch A.D. Stoyenko S. Chen Real-Time Computing Laboratory Departmentof Computer and Information Science New Jersey Institute of Technology Newark NJ USA

The authors apply random neural networks to the problem of assigning abstract data type modules (ADTs) to the processing elements of parallel computers. Although assignment of tasks has been discussed extensively in the literature, the automatic assignment of ADTs is a relatively new problem, and therefore they describe the problem in detail. This is followed by an introduction to the random neural network model and a presentation of the neural network solution to the assignment problem. Experimental results are presented comparing the solution to those obtained with random assignment and with a greedy heuristic. The random neural network is found to give significantly better results than the other two approaches in virtually every case.< >

关键词： neural networks Data structures Parallel processing Computer networks Costs Concurrent computing distributed computing Recurrent neural networks Laboratories Information science

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Signal classification for distributed decision networks with uncertainties and unmodeled class distributions

Signal classification for distributed decision networks with...

引用

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

作者： T.M. Payne University of Adelaide SA Australia

An approach is presented for the classification of a signal in noise. The classifier presented forms the bottom level in a decision network. By determining an interval of doubt about classifications, it is possible to make decisions at higher levels with additional or conflicting evidence, without having biased the decision from a low level classification. The region of uncertainty is a function of the information, so that the quality of decisions from individual decision makers will vary with the input. The decisions are formed in a parallel network which has a similar connectivity to an artificial neural network.< >

关键词： Pattern classification Uncertainty Decision making Surveillance Australia Artificial neural networks Optimal control Transportation Entropy Laboratories

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Relaxation labeling processes for the traveling salesman problem

Relaxation labeling processes for the traveling salesman pro...

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International Joint conference on neural networks (IJCNN)

作者： M. Pelillo Dipartimento di Informatica UniversitÀ di Bari Bari Italy

Relaxation labeling processes are a class of parallel distributed processing models developed to reduce local ambiguities and achieve global consistency in labeling problems. They have become a standard technique in the computer vision domain, and possess certain common properties with both artificial and biological neural systems. In particular, like the Hopfield network, they have a quadratic Lyapunov function when a symmetry condition is satisfied. In this paper the use of relaxation processes to solve the traveling salesman problem is proposed and it is quantitatively demonstrated that the algorithm is extremely effective both in finding legitimate problem solutions and in discovering optimal tours.

关键词： Labeling Traveling salesman problems Biological system modeling distributed processing Computer vision Humans Biology computing Optimization methods Circuits Biological neural networks

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neural computing: a new route to software reliability

Neural computing: a new route to software reliability

引用

1993 Third International conference on Artificial neural networks

作者： D. Partridge N. Sharkey University of Exeter UK

neural networks, parallel distributed processing, or connectionism has been the focus of a rising new paradigm in artificial intelligence with sometimes a claimed association with neuroanatomy. If a trained network is viewed simply as an implementation of a function, it has to be admitted that it is a nonconventional implementation and is obtained by a nonconventional route. networks may then be viewed as a radically different implementation of software engineering problems. In this paper the authors illustrate the potential for this viewpoint with the results of studies currently being pursued on the use of neural computing as an emergent technology for the software engineer. It is shown that 'network programming' constitutes a new programming paradigm for the software engineer, and it makes drastic changes to the traditional area on which it impacts-in this case software production. They demonstrate how certain software-engineering problems can be reliably implemented as multiversion, neural-network software systems.< >

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neural network computation in a parallel multiprocessor architecture

Neural network computation in a parallel multiprocessor arch...

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International Joint conference on neural networks (IJCNN)

作者： P. Kotilainen J. Saarinen K. Kaski Microelectronics Laboratory Tampere University of Technology Tampere Finland

A parallel multiprocessor architecture for general-purpose neurocomputing applications is introduced. Methods to map the multilayer perceptron, Kohonen's self-organising feature map and Kanerva's sparse distributed memory to the suggested architecture are discussed. The mapping examples include both the forward operation and training phase of the networks. The computational performance of the architecture is estimated for these three example cases.

关键词： neural networks Computer networks Concurrent computing Intelligent networks Computer architecture Parallel processing Neurons Signal processing algorithms Communication networks Digital signal processors

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Cellular associative memory

Cellular associative memory

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International Joint conference on neural networks (IJCNN)

作者： J. Vanhala K. Kaski Microelectronics Laboratory Tampere University of Technology Tampere Finland

A new content addressable associative memory structure, cellular associative memory, is proposed. It is composed of simple associative memory cells that are locally interconnected as a regular cellular network. This paper describes broadly the ideas behind cellular associative memory and gives an example in the field of character recognition. The properties of cellular associative memory are compared to those of the Kanerva's sparse distributed memory (1988) and the Hopfield network.

关键词： Associative memory Cellular neural networks neural networks Computer aided manufacturing CADCAM Cellular networks Chemical elements Image processing Random access memory Costs

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