A Dynamic Branch Predictor Based on Parallel Structure of SRNN
IEEE Access, 2020 Branch predictor is a key component of processor, which can improve the efficiency of instruction execution. The branch predictor based on machine learning algorithm can achieve high branch prediction accuracy, but it has the disadvantages of long ...
Lei Zhang +4 more
doaj +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
core +1 more source
Fully Convolutional Sequence Recognition Network for Water Meter Number Reading
IEEE Access, 2019 One of the most widely used frameworks for image-based sequence recognition is the convolutional recurrent neural network, which uses a convolutional neural network (CNN) for feature extraction and a recurrent neural network (RNN) for sequence modeling ...
Fan Yang +4 more
doaj +1 more source
Short term prediction of wireless traffic based on tensor decomposition and recurrent neural network
SN Applied Sciences, 2021 This paper proposes a wireless network traffic prediction model based on Bayesian Gaussian tensor decomposition and recurrent neural network with rectified linear unit (BGCP-RNN-ReLU model), which can effectively predict the changes in the upstream and ...
Tao Deng +5 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
core +1 more source
Recurrent neural network optimization for wind turbine condition prognosis
, 2022 This research focuses on employing Recurrent Neural Networks (RNN) to prognosis a wind turbine operation’s health from collected vibration time series data, by using several memory cell variations, including Long Short Time Memory (LSTM), Bilateral LSTM (
Kerboua Adlen, Kelaiaia Ridha
core +1 more source
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of ...
Shuai Li 0005 +4 more
openaire +3 more sources
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
Molecular Generation with Recurrent Neural Networks (RNNs)
CoRR, 2017 The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds. This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger ...
Esben Jannik Bjerrum, Richard Threlfall
openaire +2 more sources
What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption Generator? [PDF]
Proceedings of the 10th International Conference on Natural Language Generation, 2017 Appears in: Proceedings of the 10th International Conference on Natural Language Generation (INLG'17)
Tanti, Marc +3 more
openaire +4 more sources