[HTML][HTML] Recurrent neural networks: A comprehensive review of architectures, variants, and applications
Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …
(ML) by enabling the effective processing of sequential data. This paper provides a …
Computational complexity evaluation of neural network applications in signal processing
In this paper, we provide a systematic approach for assessing and comparing the
computational complexity of neural network layers in digital signal processing. We provide …
computational complexity of neural network layers in digital signal processing. We provide …
Performance versus complexity study of neural network equalizers in coherent optical systems
We present the results of the comparative performance-versus-complexity analysis for the
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
several types of artificial neural networks (NNs) used for nonlinear channel equalization in …
Remaining useful life estimation for prognostics of lithium-ion batteries based on recurrent neural network
M Catelani, L Ciani, R Fantacci… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Prognostic and condition-based maintenance of lithium-ion batteries is a fundamental topic,
which is rapidly expanding since a long battery lifetime is required to ensure economic …
which is rapidly expanding since a long battery lifetime is required to ensure economic …
Prediction of chaotic time series using recurrent neural networks and reservoir computing techniques: A comparative study
In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …
traditional time-series forecasting approaches in many contexts, including univariate and …
Artificial neural networks for photonic applications—from algorithms to implementation: tutorial
This tutorial–review on applications of artificial neural networks in photonics targets a broad
audience, ranging from optical research and engineering communities to computer science …
audience, ranging from optical research and engineering communities to computer science …
Physical reservoir computing with emerging electronics
Physical reservoir computing is a form of neuromorphic computing that harvests the dynamic
properties of materials for high-efficiency computing. A wide range of physical systems can …
properties of materials for high-efficiency computing. A wide range of physical systems can …
A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting
M Ishaq, S Kwon - Sustainable Energy Technologies and Assessments, 2022 - Elsevier
The integration of renewable energy generation presented an important development
around the globe and conveys countless financial, commercial, and environmental …
around the globe and conveys countless financial, commercial, and environmental …
Ensemble deep random vector functional link neural network for regression
Inspired by the ensemble strategy of machine learning, deep random vector functional link
(dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different …
(dRVFL), and ensemble dRVFL (edRVFL) has shown state-of-the-art results on different …
Divide and conquer: Learning chaotic dynamical systems with multistep penalty neural ordinary differential equations
Forecasting high-dimensional dynamical systems is a fundamental challenge in various
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …
fields, such as geosciences and engineering. Neural Ordinary Differential Equations …