Multi-step learning rule for recurrent neural models: an application to time series forecasting [PDF]
, 2001 Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information.
Galván, Inés M. +3 more
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Recurrent Neural Network-Based Nonlinear Optimization for Braking Control of Electric Vehicles
Energies, 2022 In this paper, electro-hydraulic braking (EHB) force allocation for electric vehicles (EVs) is modeled as a constrained nonlinear optimization problem (NOP).
Jiapeng Yan, Huifang Kong, Zhihong Man
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Memory augmented recurrent neural networks for de-novo drug design.
PLoS ONE, 2022 A recurrent neural network (RNN) is a machine learning model that learns the relationship between elements of an input series, in addition to inferring a relationship between the data input to the model and target output.
Naveen Suresh +3 more
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ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION
Jurnal Ilmiah SINERGI, 2020 Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.
Widi Aribowo
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Sparse RNNs can support high-capacity classification.
PLoS Computational Biology, 2022 Feedforward network models performing classification tasks rely on highly convergent output units that collect the information passed on by preceding layers.
Denis Turcu, L F Abbott
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Applications of recurrent neural networks in batch reactors. Part I: NARMA modelling of the dynamic behaviour of the heat transfer fluid [PDF]
, 1997 This paper is focused on the development of nonlinear models, using artificial neural networks, able to provide appropriate predictions when acting as process simulators. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical
J.M. Zaldı́var +3 more
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Human EEG and Recurrent Neural Networks Exhibit Common Temporal Dynamics During Speech Recognition
Frontiers in Systems Neuroscience, 2021 Recent deep-learning artificial neural networks have shown remarkable success in recognizing natural human speech, however the reasons for their success are not entirely understood.
Saeedeh Hashemnia +3 more
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Exponential stability of delayed recurrent neural networks with Markovian jumping parameters [PDF]
, 2006 This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent ...
Liu, Y, Yu, L, Wang, Z, Iu, X
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Recurrent neural network wind power prediction based on variational modal decomposition improvement
AIP Advances, 2023 In order to avoid the problem that the traditional recurrent neural network (RNN) wind power prediction model cannot take into account both the law of wind power variation and the impact of sudden change factors, this paper proposes an improved cyclic ...
Chongqing Shi, Xiaoli Zhang
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Energies, 2023 This article aims to provide a technique for identifying and categorizing interturn insulation problems in variable-speed motor drives by combining Salp Swarm Optimization (SSO) with Recurrent Neural Network (RNN).
Gopu Venugopal +7 more
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