Results 251 to 260 of about 1,858,056 (293)

From chaos to clock in recurrent neural net. Case study

Biosystems, 2022
What is the reason for complex dynamical patterns registered from real biological neuronal networks? Noise and dynamical reconfiguring of a network (functional/dynamic connectome) were proposed as possible answers. In this case study, we report a complex dynamical pattern observed in a simple deterministic network of 25 excitatory neurons with fixed ...
Alexander K. Vidybida, Olha Shchur
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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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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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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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Recurrent neural nets as dynamical Boolean systems with application to associative memory

IEEE Transactions on Neural Networks, 1997
Discrete-time/discrete-state recurrent neural networks are analyzed from a dynamical Boolean systems point of view in order to devise new analytic and design methods for the class of both single and multilayer recurrent artificial neural networks. With the proposed dynamical Boolean systems analysis, we are able to formulate necessary and sufficient ...
Paul Watta, Kaining Wang, Mohamad H. Hassoun   +2 more
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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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