Results 11 to 20 of about 317,020 (282)
Fuzzy-Weighted Echo State Networks
Frontiers in Energy Research, 2022 A novel echo state network (ESN), referred to as a fuzzy-weighted echo state network (FWESN), is proposed by using the structural information of data sets to improve the performance of the classical ESN. The information is incorporated into the classical
Zhao Yao, Zhao Yao, Yingshun Li
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Physics-informed echo state networks [PDF]
Journal of Computational Science, 2020 We propose a physics-informed Echo State Network (ESN) to predict the evolution of chaotic systems. Compared to conventional ESNs, the physics-informed ESNs are trained to solve supervised learning tasks while ensuring that their predictions do not violate physical laws.
Doan, Nguyen Anh Khoa +2 more
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Consistency in echo-state networks [PDF]
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019 Consistency is an extension to generalized synchronization which quantifies the degree of functional dependency of a driven nonlinear system to its input. We apply this concept to echo-state networks, which are an artificial-neural network version of reservoir computing.
Thomas Lymburn +5 more
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Echo state networks are universal [PDF]
Neural Networks, 2018 This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state ...
Grigoryeva, Lyudmila, Ortega, Juan-Pablo
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Applied Sciences, 2022 Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time
Wen-Jie Liu +4 more
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Sequence Prediction and Classification of Echo State Networks
Mathematics, 2023 The echo state network is a unique form of recurrent neural network. Due to its feedback mechanism, it exhibits superior nonlinear behavior compared to traditional neural networks and is highly regarded for its simplicity and efficiency in computation ...
Jingyu Sun, Lixiang Li, Haipeng Peng
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Tree-structured Data Processing Method Based on Model Space [PDF]
Jisuanji gongcheng, 2017 Classical machine learning methods are not enough for dealing with tree data because the tree contains not only node information but also structure information.Therefore,this paper proposes an approach of tree echo state network,applicated to tree ...
DONG Yadong,LI Zhengyu,WANG Yang
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On the Post Hoc Explainability of Optimized Self-Organizing Reservoir Network for Action Recognition
Sensors, 2022 This work proposes a novel unsupervised self-organizing network, called the Self-Organizing Convolutional Echo State Network (SO-ConvESN), for learning node centroids and interconnectivity maps compatible with the deterministic initialization of Echo ...
Gin Chong Lee, Chu Kiong Loo
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Enhancing EEG-based emotion recognition using PSD-Grouped Deep Echo State Network [PDF]
Journal of Universal Computer Science, 2023 Emotions are a crucial aspect of daily life and play a vital role in shaping human inter-actions. The purpose of this paper is to introduce a novel approach to recognize human emotions through the use of electroencephalogram (EEG) signals.
Samar Bouazizi +2 more
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Probabilistic modelling of substorm occurrences with an echo state network [PDF]
Annales Geophysicae, 2023 The relationship between solar-wind conditions and substorm activity is modelled with an approach based on an echo state network. Substorms are a fundamental physical phenomenon in the magnetosphere–ionosphere system, but the deterministic prediction of ...
S. Nakano +7 more
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