Results 271 to 280 of about 3,951 (305)

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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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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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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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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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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Schema generation in recurrent neural nets for intercepting a moving target

Biological Cybernetics, 2010
The grasping of a moving object requires the development of a motor strategy to anticipate the trajectory of the target and to compute an optimal course of interception. During the performance of perception-action cycles, a preprogrammed prototypical movement trajectory, a motor schema, may highly reduce the control load.
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A Myocardial T1-Mapping Framework with Recurrent and U-Net Convolutional Neural Networks

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020
Noise and aliasing artifacts arise in various accelerated cardiac magnetic resonance (CMR) imaging applications. In accelerated myocardial T1-mapping, the traditional three-parameter based nonlinear regression may not provide accurate estimates due to sensitivity to noise. A deep neural network-based framework is proposed to address this issue.
Haris Jeelani, Yang Yang 0110, Ruixi Zhou, Christopher M. Kramer, Michael Salerno, Daniel S. Weller   +5 more
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Energy-Time Tradeoff in Recurrent Neural Nets

2015
In this chapter, we deal with the energy complexity of perceptron networks which has been inspired by the fact that the activity of neurons in the brain is quite sparse (with only about 1% of neurons firing). This complexity measure has recently been introduced for feedforward architectures (i.e., threshold circuits).
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GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation

Medical Image Analysis, 2019
Cell segmentation in microscopy images is a common and challenging task. In recent years, deep neural networks achieved remarkable improvements in the field of computer vision. The dominant paradigm in segmentation is using convolutional neural networks, less common are recurrent neural networks.
Thomas Wollmann, Manuel Gunkel, Inn Chung, Holger Erfle, Karsten Rippe, Karl Rohr   +5 more
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Computer Simulations of Recurrent Neural Nets for Temporal Recognition Problems

1992
A number of approaches are presented to the use of neural nets with feedback to handle the recognition of temporal data and these are assessed with respect to resilience (ability to handle noisy or incomplete input) and performance in handling similar and overlapping patterns.
G. S. Cooper, T. M. Child
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