Time series prediction using deep echo state networks
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04948-x
Abstract: Artificial neural networks have been used for time series modeling and forecasting in many domains. However, they are often limited in their handling of nonlinear and chaotic data. More recently, reservoir-based recurrent neural net systems,… read more here.
Keywords: echo state; state networks; time series; time ... See more keywords
Optimizing Echo State Networks for Static Pattern Recognition
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DOI: 10.1007/s12559-017-9468-2
Abstract: Static pattern recognition requires a machine to classify an object on the basis of a combination of attributes and is typically performed using machine learning techniques such as support vector machines and multilayer perceptrons. Unusually,… read more here.
Keywords: static pattern; echo state; classification; pattern recognition ... See more keywords
Proton exchange membrane fuel cell ageing forecasting algorithm based on Echo State Network
Sign Up to like & getrecommendations! Published in 2017 at "International Journal of Hydrogen Energy"
DOI: 10.1016/j.ijhydene.2016.05.286
Abstract: Regarded as a promising technology, proton exchange membrane fuel cell (PEMFC) are not far from a large-scale deployment. However, some improvements are still needed to extend the lifetime of these systems. The discipline of PHM… read more here.
Keywords: proton exchange; fuel cell; network; exchange membrane ... See more keywords
Generating probabilistic predictions using mean-variance estimation and echo state network
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DOI: 10.1016/j.neucom.2016.09.064
Abstract: In conventional time series prediction techniques, uncertainty associated with predictions are usually ignored. Probabilistic predictors, on the other hand, can measure the uncertainty in predictions, to provide better supports for decision-making processes. A dynamic probabilistic… read more here.
Keywords: echo state; mean variance; variance estimation; variance ... See more keywords
Robust echo state networks based on correntropy induced loss function
Sign Up to like & getrecommendations! Published in 2017 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.05.087
Abstract: Abstract In this paper, a robust echo state network with correntropy induced loss function (CLF) is presented. CLF is robust to outliers through the mechanism of correntropy which is widely applied in information theoretic learning.… read more here.
Keywords: state; echo state; robust echo; loss function ... See more keywords
Adaptive echo state network control for a class of pure-feedback systems with input and output constraints
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DOI: 10.1016/j.neucom.2017.09.083
Abstract: Abstract In this paper, an adaptive echo state network control scheme is proposed for a class of constrained pure-feedback systems, in which both input and output constraints are considered simultaneously. A prescribed performance function characterizing… read more here.
Keywords: state; echo state; control; state network ... See more keywords
Local Lyapunov exponents of deep echo state networks
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2017.11.073
Abstract: Abstract The analysis of deep Recurrent Neural Network (RNN) models represents a research area of increasing interest. In this context, the recent introduction of Deep Echo State Networks (DeepESNs) within the Reservoir Computing paradigm, enabled… read more here.
Keywords: deep echo; local lyapunov; state networks; echo state ... See more keywords
An echo state network based approach to room classification of office buildings
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.12.033
Abstract: Abstract Office buildings commonly contain such room types as office rooms, server rooms, storage rooms, meeting rooms, etc., while the power consumption inside the rooms generally comes from appliances, lights and air-conditioners. Based on the… read more here.
Keywords: office; power consumption; echo state; office buildings ... See more keywords
Echo state network optimization using binary grey wolf algorithm
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DOI: 10.1016/j.neucom.2019.12.069
Abstract: Abstract The echo state network (ESN) is a powerful recurrent neural network for time series modelling. ESN inherits the simplified structure and relatively straightforward training process of conventional neural networks, and shows strong computational capabilities… read more here.
Keywords: network; grey wolf; binary grey; state network ... See more keywords
Echo state kernel recursive least squares algorithm for machine condition prediction
Sign Up to like & getrecommendations! Published in 2018 at "Mechanical Systems and Signal Processing"
DOI: 10.1016/j.ymssp.2018.03.047
Abstract: Abstract Kernel adaptive filter (KAF) has been widely utilized for time series prediction due to its online adaptation scheme, universal approximation capability and convexity. Nevertheless, KAF’s ability to handle temporal tasks is limited, because it… read more here.
Keywords: state; recursive least; least squares; kernel recursive ... See more keywords
Application of echo state networks for estimating voltage harmonic waveforms in power systems considering a photovoltaic system
Sign Up to like & getrecommendations! Published in 2017 at "Iet Renewable Power Generation"
DOI: 10.1049/iet-rpg.2017.0046
Abstract: An estimator based on echo state networks (ESNs) is presented in this study to estimate voltage harmonic distortion waveforms at non-monitored sensitive loads using current and voltage at a monitored location. Since distributed generations such… read more here.
Keywords: voltage; voltage harmonic; power; state networks ... See more keywords