Results 181 to 190 of about 98,687 (237)
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Asynchronous translations with recurrent neural nets
Proceedings of International Conference on Neural Networks (ICNN'97), 1997Many 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.
R. Ñeco, M. Forcada
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Energy-Time Tradeoff in Recurrent Neural Nets
, 2015In 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).
Jirí Síma
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The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998Recurrent nets are in principle capable to store past inputs to produce the currently desired output. Because of this property recurrent nets are used in time series prediction and process control. Practical applications involve temporal dependencies spanning many time steps, e.g. between relevant inputs and desired outputs.
Sepp Hochreiter
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Forecasting Ozone Pollution using Recurrent Neural Nets and Multiple Quantile Regression
2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON), 2019Due to its harmful effects on human health and agriculture, ground-level ozone concentrations are continually monitored nowadays in most places in the world.
Daniel Flores-Vergara +5 more
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Prediction of software reliability using feedforward and recurrent neural nets
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1992The 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, L. D. Whitley
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Recurrent neural nets as dynamical Boolean systems with application to associative memory
IEEE Transactions on Neural Networks, 1997Discrete-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 ...
P. Watta, Kaining Wang, M. Hassoun
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Evolutionary Chromatographic Law Identification by Recurrent Neural Nets
Evolutionary Programming, 1995A. Fadda, Marc Schoenauer
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From chaos to clock in recurrent neural net. Case study
Biosystems, 2022What 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 ...
Vidybida, A., Shchur, Olha
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On the identification of recurrent neural nets
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002Observational 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.
D. Trummer, R. Deistler
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Performance Analysis for Virtual-Cell Based CoMP 5G Networks Using Deep Recurrent Neural Nets
Wireless Telecommunications Symposium, 2019Providing high date rates that are independent of user location in the network is one of the Fifth Generation (5G) wireless network goals. This goal becomes even more challenging when the mobility of users is taken into account.
Mohamed Elkourdi, Asim Mazin, R. Gitlin
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