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Artificial neural networks implementation on vectorial supercomputers

Artificial neural networks implementation on vectorial super...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： E. Sanchez S. Barro C.V. Regueiro Departamento de electrónica y computación. Facultad de Fisica Universidade de Santiago de Compostela Santiago de Compostela Spain

We present the results from the implementation of a multilayer perceptron with the backpropagation algorithm on a FUJITSU VP-2400/10 vectorial supercomputer. The programming methodology employed, tries to obtain the maximum performance in this kind of machines based on: input/output structures, treatment of conditional statements, do-loops vectorization and compiler directives. In this work, computing times for the VU (vectorial unit) and CPU are presented. These times indicate the possibility of real time operation in applications which demand artificial neural networks with a high structural complexity.< >

关键词： Artificial neural networks Supercomputers Neurons Multilayer perceptrons Nonhomogeneous media backpropagation algorithms Speech recognition Computer networks Very large scale integration Equations

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Optimization of trading systems and portfolios

Optimization of trading systems and portfolios

引用

Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering (CIFEr)

作者： J. Moody Lizhong Wu CSE Department Oregon Graduate Institute Portland OR USA

We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning algorithms. The objective functions that we consider as evaluation functions for reinforcement learning are profit or wealth, economic utility, the Sharpe ratio, and our proposed Differential Sharpe Ratio. The trading and portfolio management systems require prior decisions as input in order to properly take into account the effects of transactions costs, market impact, and taxes. This temporal dependence on system state requires the use of reinforcement versions of standard recurrent learning algorithms. We present empirical results in controlled experiments that demonstrate the efficacy of some of our methods. We find that maximizing the differential Sharpe ratio yields more consistent results than maximizing profits, and that both methods outperform a trading system based on forecasts that minimize MSE.

关键词： Portfolios Economic forecasting Costs Investments Supervised learning Finance Labeling backpropagation algorithms Management training Optimization methods

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A neural network approach for the control of a tracking behavior

A neural network approach for the control of a tracking beha...

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International Conference on Advanced Robotics (ICAR)

作者： K. Berns R. Dillmann Gruppe Interaktive Planungstechnik Forschungszentrum Informatik an der Universität Karlsruhe Karlsruhe Germany

The problem of correctly evaluating noisy and incorrect data for the interpretation of ultrasonic sensor signals is an often encountered one. Neural networks, with their inherent characteristics of adaptivity and high fault- and noise-tolerance are well suited for such tasks. In this paper two neural network approaches are described for the control of the tracking behavior of an autonomous mobile robot. Input data are provided by a set of ultrasonic sensors mounted at the front of the vehicle. Two neural network learning strategies: backpropagation and reinforcement learning, are examined in a simulation and compared with respect to learning speed, capacity of tracking and the effort required to adapt the control networks of the real vehicle.< >

关键词： Neural networks Sensor phenomena and characterization Mobile robots Remotely operated vehicles Learning Ultrasonic variables measurement Sensor systems backpropagation algorithms Testing Telephony

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An observation concerning a classification problem and back-propagation for the feedforward neural network

An observation concerning a classification problem and back-...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： E. Sakk J. Belina R.J. Thomas School of Electrical Engineering Cornell University Ithaca NY USA

A simple classification problem using a single-layer feedforward neural network in conjunction with the backpropagation training algorithm (BPTA) is examined. It has been observed that, for such a problem, the values of the input weights are closely related to the input training set. An implication of this observation is that, rather than choosing initially random weights for the BPTA, one may choose initial weights that are actually quite close to a global minimum in the BP error function. An advantage of such a choice would be faster convergence times based on knowledge of the incoming training data.< >

关键词： Neurons Neural networks Feedforward neural networks Convergence backpropagation algorithms Electrocardiography Training data Algorithm design and analysis Erbium Acceleration

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On using an artificial feedforward neural network as a controller in large-scale power systems

On using an artificial feedforward neural network as a contr...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： R.J. Thomas E. Sakk School of Electrical Engineering Cornell University Ithaca NY USA

The authors examine the usefulness of the feedforward neural network as a controller. In particular, the authors consider as an example the problem of design of a power system stabilizer that has the structure of a classical feedforward artificial neural network. In this case, the controller is developed for a class of problems associated with linear plants. Certain observations are made regarding the generic behavior of systems with this type of controller structure. The authors incorporate a robustness criterion into the design as well as a conventional dynamic performance criterion.< >

关键词： Artificial neural networks Neural networks Feedforward neural networks Power system control Control systems Intelligent networks Large-scale systems Power systems backpropagation algorithms Vectors

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Tactile pattern recognition using neural networks

Tactile pattern recognition using neural networks

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International Conference on Industrial Electronics, Control and Instrumentation

作者： W.S. McMath M.D. Colven S.K. Yeung E.M. Petriu Canadian Space Agency Ottawa ON Canada Department of Electrical Engineering University of Ottawa Ottawa ON Canada

This paper discusses an experimental tactile sensor based on force sensitive transducer technology and its application for pattern recognition. A multilayer neural network using the backpropagation algorithm was train... 详细信息

关键词： Pattern recognition Neural networks Tactile sensors Force sensors Transducers Multi-layer neural network backpropagation algorithms Image recognition Humans Force measurement

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Bistatic fixed-receiver parasitic SAR processor based on the back-propagation algorithm

Bistatic fixed-receiver parasitic SAR processor based on the...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： J. Sanz-Marcos P. Prats J.J. Mallorqui Signal Theory and Communications Department Universitat Politecnica de Catalunya Barcelona Spain

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Determining the influence of input parameters on BP neural network output error using sensitivity analysis

Determining the influence of input parameters on BP neural n...

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International Conference on Computational Intelligence and Multimedia Applications

作者： F. Frost V. Karri DCC Offices Comalco Aluminium Limited Bell Bay TAS Australia School of Science and Engineering University of Tasmania Hobart TAS Australia

Neural networks, due to their excellent capabilities for modelling process behaviour, are gaining precedence over traditional empirical modelling techniques, such as statistical methods. While neural networks have good ability to map any reasonable continuous function, they do not easily explain how the inputs are related to an output, and also whether the selected inputs have any significant relationship with an output. There is quite often a need to identify some order of influence of the input variables on the output variable. In this paper, a technique for determining the order of influence of the n elements of the input vector on the m elements of the output vector is presented and discussed. While a sample mathematical function is used to introduce the technique, a more practical application of this method in the aluminium smelting industry is considered. It is shown that, using a sensitivity analysis on the backpropagation (BP) algorithm, the degree of influence of the input parameters on the output error can be successfully estimated.

关键词： Neural networks Sensitivity analysis Input variables Training data Aluminum Australia Sorting Metals industry backpropagation algorithms Manufacturing

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KORA-2: a new cache replacement policy and its performance

KORA-2: a new cache replacement policy and its performance

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IEEE International Conference on Electronics, Circuits and Systems (ICECS)

作者： H. Khalid M.S. Obaidat KFUPM and Motorola Monmouth University USA

In this paper, we propose a new strategy (KORA-2) for the replacement of lines in cache memories. The algorithm is efficient and easily implementable. Trace-driven simulations were performed for 42 different cache configurations using benchmark programs from SPEC92 (Standard performance Evaluation Corporation) benchmark suites. Simulation results illustrate that our algorithm can provide a peak value of approximately 8.71% improvement in the miss ratio over the best performing conventional algorithm (LRU) for the selected benchmark trace files generated from SPEC programs. This translates to a savings of hundreds of thousands of misses for typical programs referencing well over 100 million addresses.

关键词： Neural networks backpropagation algorithms Neurons Cache memory High performance computing History Electronic mail Performance evaluation Fuzzy logic Resource management

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Optimal pruning of neural tree networks for improved generalization

Optimal pruning of neural tree networks for improved general...

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International Joint Conference on Neural Networks (IJCNN)

作者： A. Sankar R.J. Mammone CAIP Center and Department of Electrical Engineering Rutgers University Piscataway NJ USA

An optimal pruning algorithm for neural tree networks (NTN) is presented. The NTN is grown by a constructive learning algorithm that decreases the classification error on the training data recursively. The optimal pruning algorithm is then used to improve generalization. The pruning algorithm is shown to be computationally inexpensive. Simulation results on a speaker-independent vowel recognition task are presented to show the improved generalization using the pruning algorithm.< >

关键词： Neural networks Neurons Training data Tree data structures Classification tree analysis Speech recognition backpropagation algorithms Binary trees Classification algorithms Feedforward neural networks

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