Results 41 to 50 of about 233,583 (184)
Distance-Based Delays in Echo State Networks
, 2022 Physical reservoir computing, a paradigm bearing the promise of energy-efficient high-performance computing, has raised much attention in recent years. We argue though, that the effect of signal propagation delay on reservoir task performance, one of the most central aspects of physical reservoirs, is still insufficiently understood in a more general ...
Stefan Iacob +2 more
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Hyperparameter tuning in echo state networks
Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Echo State Networks represent a type of recurrent neural network with a large randomly generated reservoir and a small number of readout connections trained via linear regression. The most common topology of the reservoir is a fully connected network of up to thousands of neurons.
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Deterministic Echo State Networks Based Stock Price Forecasting
Abstract and Applied Analysis, 2014 Echo state networks (ESNs), as efficient and powerful computational models for approximating nonlinear dynamical systems, have been successfully applied in financial time series forecasting.
Jingpei Dan +4 more
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Comparison of Resting-State Brain Activation Detected by BOLD, Blood Volume and Blood Flow
Frontiers in Human Neuroscience, 2018 Resting-state brain activity has been widely investigated using blood oxygenation level dependent (BOLD) contrast techniques. However, BOLD signal changes reflect a combination of the effects of cerebral blood flow (CBF), cerebral blood volume (CBV), as ...
Ke Zhang +3 more
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Convolutional Echo‐State Network with Random Memristors for Spatiotemporal Signal Classification
Advanced Intelligent Systems, 2022 The unprecedented development of Internet of Things results in the explosion of spatiotemporal signals generated by smart edge devices, leading to a surge of interest in real‐time learning of such data.
Shaocong Wang +19 more
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AI, 2022 Noise of any kind can be an issue when translating results from simulations to the real world. We suddenly have to deal with building tolerances, faulty sensors, or just noisy sensor readings.
Christoph Walter Senn, Itsuo Kumazawa
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Time Series Prediction With Incomplete Dataset Based on Deep Bidirectional Echo State Network
IEEE Access, 2019 In the complex industrial environment, data missing situation is often occurred in the process of data acquisition and transition. The major contribution of the paper is the proposal of a deep bidirectional echo state network (DBESN) framework for time ...
Qiang Wang +4 more
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Using Echo State Networks for Cryptography
, 2017 Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data.
Bauckhage, Christian +3 more
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Gated Echo State Networks: a preliminary study [PDF]
2020 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2020 Gating mechanisms are widely used in the context of Recurrent Neural Networks (RNNs) to improve the network's ability to deal with long-term dependencies within the data. The typical approach for training such networks involves the expensive algorithm of gradient descent and backpropagation.
Di Sarli D., Gallicchio C., Micheli A.
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Seasonal prediction of Indian summer monsoon onset with echo state networks
Environmental Research Letters, 2021 Although the prediction of the Indian Summer Monsoon (ISM) onset is of crucial importance for water-resource management and agricultural planning on the Indian sub-continent, the long-term predictability—especially at seasonal time scales—is little ...
Takahito Mitsui, Niklas Boers
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