Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
IEEE Transactions on Smart Grid, 2019 As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers ...
Weicong Kong +5 more
semanticscholar +1 more source
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values [PDF]
Transportation Research Part C: Emerging Technologies, 2020 Short-term traffic forecasting based on deep learning methods, especially recurrent neural networks (RNN), has received much attention in recent years. However, the potential of RNN-based models in traffic forecasting has not yet been fully exploited in ...
Zhiyong Cui +3 more
semanticscholar +1 more source
Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs [PDF]
, 2001 Haschke R, Steil JJ, Ritter H. Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs. In: Dorffner G, Bischof H, Hornik K, eds. Artificial Neural Networks - ICANN 2001. Lecture notes in computer science.
Dorffner, Georg +5 more
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Comparison of Neural Network and Recurrent Neural Network to Predict Rice Productivity in East Java
JITeCS (Journal of Information Technology and Computer Science), 2021 Rice is the staple food for most of the population in Indonesia which is processed from rice plants. To meet the needs and food security in Indonesia, a prediction is required.
Andi Hamdianah +2 more
doaj +1 more source
Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. [PDF]
, 2008 In this paper, a continuous time recurrent neural network (CTRNN) is developed to be used in nonlinear model predictive control (NMPC) context. The neural network represented in a general nonlinear state-space form is used to predict the future ...
Cao, Yi, Al Seyab, Rihab Khalid Shakir
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SeqSleepNet: End-to-End Hierarchical Recurrent Neural Network for Sequence-to-Sequence Automatic Sleep Staging [PDF]
IEEE transactions on neural systems and rehabilitation engineering, 2018 Automatic sleep staging has been often treated as a simple classification problem that aims at determining the label of individual target polysomnography epochs one at a time.
Huy Phan +4 more
semanticscholar +1 more source
Prediction of Convergence Dynamics of Design Performance using Differential Recurrent Neural Networks [PDF]
, 2008 Computational Fluid Dynamics (CFD) simulations have been extensively used in many aerodynamic design optimization problems, such as wing and turbine blade shape design optimization.
Sendhoff, Bernhard +12 more
core +1 more source
A feed forward neural network approach for matrix computations [PDF]
, 2001 This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new neural network approach for performing matrix computations is presented. The idea of this approach is to construct a feed-forward neural network (FNN)
Al-Mudhaf, Ali F
core +7 more sources
HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
Processes, 2021 Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks ...
Muhammad Ashfaq Khan
semanticscholar +1 more source
Shuffling Recurrent Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 We propose a novel recurrent neural network model, where the hidden state hₜ is obtained by permuting the vector elements of the previous hidden state hₜ₋₁ and adding the output of a learned function β(xₜ) of the input xₜ at time t. In our model, the prediction is given by a second learned function, which is applied to the hidden state s(hₜ).
Michael Rotman, Lior Wolf
openaire +2 more sources