Results 221 to 230 of about 3,556 (261)

On the identification of recurrent neural nets

Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002
Observational equivalence for so-called Jordan networks, which are a special class of recurrent networks, is analysed. We show this type of neural nets to belong to a wider class of mixed networks and use the description of observational equivalence available for the latter class for obtaining the respective results for the first class.
Dietmar Trummer, Manfred Deistler
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Asynchronous translations with recurrent neural nets

Proceedings of International Conference on Neural Networks (ICNN'97), 2002
Many researchers have explored the relation between discrete-time recurrent neural networks (DTRNN) and finite-state machines (FSMs) either by showing their computational equivalence or by training them to perform as finite-state recognizers from examples.
Ramón P. Ñeco, Mikel L. Forcada
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Modelling Landsurface Time-Series with Recurrent Neural Nets

IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018
Machine learning tools and semi-empirical models have been very successful in describing and predicting instantaneous climatic influences on the spatial and seasonal variability of biosphere state and function. Yet, little work has been carried to explicitly model dynamic features accounting for memory effects, where in some cases hand-designed ...
Markus Reichstein, Simon Besnard, Nuno Carvalhais, Fabian Gans, Martin Jung 0002, Basil Kraft, Miguel D. Mahecha   +6 more
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Motion analysis with recurrent neural nets

1994
218zVisual tasks such as the interpretation of cell images (Psarrou and Buxton, 1993) and the recognition of moving vehicles require to track objects along their trajectory and to predict their future position in their environment. It was noted that objects move purposely in an environment and effective prediction on their trajectories can be achieved ...
A. Psarrou, H. Buxton
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RACE-Net: A Recurrent Neural Network for Biomedical Image Segmentation

IEEE Journal of Biomedical and Health Informatics, 2019
The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner.
Arunava Chakravarty, Jayanthi Sivaswamy
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Nonlinear predictive vector quantisation with recurrent neural nets

Neural Networks for Signal Processing III - Proceedings of the 1993 IEEE-SP Workshop, 2002
The nonlinear prediction capability of neural nets is applied to the design of improved predictive speech coders. Performance evaluations and comparisons with linear predictive speech coding are presented. These tests show the applicability of nonlinear prediction to speech coding and the improvement in coding performance. >
L. Wu, M. Niranjan, F. Fallside
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Learning temporal sequences in recurrent self-organising neural nets

1997
The learning of temporal sequences is an extremely important component of human and animal behaviour. As well as the motor control involved in routine behaviour such as walking, running, talking, tool use and so on, humans have an apparently remarkable capacity for learning (and subsequently reproducing) temporal sequences. A new connectionist model of
Garry Briscoe, Terry Caelli
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A recurrent neural net approach to one-step ahead control problems

IEEE Transactions on Systems, Man, and Cybernetics, 1994
In this paper, we present a recurrent neural net technique to provide control actions for nonlinear dynamic systems. In most current neural net control approaches, two nets are usually required. One acts as a system emulator, and the other one is a controller network.
Percy P. C. Yip, Yoh-Han Pao
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Univariate Time Series Using Recurrent Neural Nets

2021
This chapter covers the basics of deep learning. First, it introduces the activation function, the loss function, and artificial neural network optimizers. Second, it discusses the sequence data problem and how a recurrent neural network (RNN) solves it. Third, the chapter presents a way of designing, developing, and testing the most popular RNN, which
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Prediction of software reliability using feedforward and recurrent neural nets

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 2003
The authors present an adaptive modeling approach based on connectionist networks and demonstrate how both feedforward and recurrent networks and various training regimes can be applied to predict software reliability. They make an empirical comparison between this new approach and five well-known software reliability growth prediction models using ...
N. Karunanithi, D. Whitley
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