scholarly journals A reservoir of timescales in random neural networks

2021 ◽  
Author(s):  
Merav Stern ◽  
Nicolae Istrate ◽  
Luca Mazzucato
Keyword(s):  
Neural Networks ◽  
Neural Circuits ◽  
Large Range ◽  
Time Dependent ◽  
Temporal Heterogeneity ◽  
Potential Mechanism ◽  
Cortical Circuits ◽  
Multiple Timescales ◽  
Spectral Components ◽  
Random Neural Networks

The temporal activity of many biological systems, including neural circuits, exhibits fluctuations simultaneously varying over a large range of timescales. The mechanisms leading to this temporal heterogeneity are yet unknown. Here we show that random neural networks endowed with a distribution of self-couplings, representing functional neural clusters of different sizes, generate multiple timescales activity spanning several orders of magnitude. When driven by a time-dependent broadband input, slow and fast neural clusters preferentially entrain slow and fast spectral components of the input, respectively, suggesting a potential mechanism for spectral demixing in cortical circuits.

scholarly journals Time-dependent prediction degredation assessment of neural-networks-based TEC forecasting models

2003 ◽  
Vol 10 (6) ◽  
pp. 585-587 ◽  
Author(s):  
Th. D. Xenos ◽  
S. S. Kouris ◽  
A. Casimiro
Keyword(s):  
Neural Networks ◽  
Faraday Rotation ◽  
Prediction Error ◽  
Prediction Accuracy ◽  
Time Span ◽  
Time Dependent ◽  
Forecasting Models ◽  
Error Margin ◽  
The Difference ◽  
Tec Variability

Abstract. An estimation of the difference in TEC prediction accuracy achieved when the prediction varies from 1 h to 7 days in advance is described using classical neural networks. Hourly-daily Faraday-rotation derived TEC measurements from Florence are used. It is shown that the prediction accuracy for the examined dataset, though degrading when time span increases, is always high. In fact, when a relative prediction error margin of ± 10% is considered, the population percentage included therein is almost always well above the 55%. It is found that the results are highly dependent on season and the dataset wealth, whereas they highly depend on the foF2 - TEC variability difference and on hysteresis-like effect between these two ionospheric characteristics.

scholarly journals A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4493
Author(s):  
Rui Silva ◽  
António Araújo
Keyword(s):  
Neural Networks ◽  
Condition Monitoring ◽  
Recurrent Neural Networks ◽  
Cutting Process ◽  
Time Dependent ◽  
Industrial Processes ◽  
Fundamental Part ◽  
Novel Approach ◽  
Noisy Signals ◽  
Proper Condition

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

Energy demand forecasting of buildings using random neural networks

2020 ◽  
Vol 38 (4) ◽  
pp. 4753-4765
Author(s):  
Jawad Ahmad ◽  
Ahsen Tahir ◽  
Hadi Larijani ◽  
Fawad Ahmed ◽  
Syed Aziz Shah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Energy Demand ◽  
Demand Forecasting ◽  
Energy Demand Forecasting ◽  
Random Neural Networks

scholarly journals Inversion of time-dependent nuclear well-logging data using neural networks

2007 ◽  
Vol 0 (0) ◽  
pp. 071031042340001-???
Author(s):  
Laura Carmine ◽  
Elsa Aristodemou ◽  
Christopher Pain ◽  
Ann Muggeridge ◽  
Cassiano de Oliveira
Keyword(s):  
Neural Networks ◽  
Well Logging ◽  
Time Dependent
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