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Neural Networks and Reservoir Computing

Echo State Queueing Networks: a combination of Reservoir Computing and Random Neural Networks

Echo State Queueing Networks: a combination of Reservoir Computing and Random Neural Networks

... 3 Reservoir Computing As recalled before, a Recurrent Neural Network is a Neural Network where there are circuits in the connections between neurons (we also say ...recurrent networks ...

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On the application of reservoir computing networks for noisy image recognition

On the application of reservoir computing networks for noisy image recognition

... Abstract Reservoir Computing Networks (RCNs) are a special type of single layer recurrent neural networks, in which the in- put and the recurrent connections are randomly generated and ...

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A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing

A Schmitt Trigger Based Oscillatory Neural Network for Reservoir Computing

... oscillatory neural network with simple external linear training weight can efficiently perform information processing in both hardware and software ...complex networks used in traditional Schmitt trigger ...

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SpaRCe : sparse reservoir computing

SpaRCe : sparse reservoir computing

... “Sparse” neural networks, in which relatively few neurons or connections are active, are common in both machine learning and ...a reservoir computing network via neuron- specific learnable ...

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Introduction to Reservoir Computing Methods

Introduction to Reservoir Computing Methods

... like Neural Networks that currently dominate the applications of automated ...Recurrent Neural Networks (chapter 2), gath- ered under the common name of Reservoir Computing, that ...

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Reservoir computing with output feedback

Reservoir computing with output feedback

... of reservoir computing with output feedback in three ...this reservoir regularization is to implement desired network dynamics with small weights which reduces the gain of the ...recurrent ...

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Enhanced reservoir computing with concatenation

Enhanced reservoir computing with concatenation

... structure, reservoir, so it is useful when we use the temporal(sequential) data, such as system data, handwritten ...a reservoir. We obtain the dynamical patterns of reservoir by perturbing input ...

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Distributed Reservoir Computing with Sparse Readouts

Distributed Reservoir Computing with Sparse Readouts

... In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion ...

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Reservoir Computing for Learning in Structured Domains

Reservoir Computing for Learning in Structured Domains

... Another relevant issue about feed-forward neural networks concerns the optimum archi- tectural design of MLPs. One solution to this problem is given by the Cascade Correlation algorithm [47], which follows ...

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On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks

... of reservoir computing started as an insight about behaviour of synaptic weights during the neural network training ...of neural network one could use an arbitrary dynamical system for ...

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The role of structure and complexity on Reservoir Computing quality

The role of structure and complexity on Reservoir Computing quality

... of Reservoir Computers ...of neural network structures, as an analogy for physical ...smaller networks by adding greater connection ...of computing substrates, as the latter typically has ...

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Neural networks for forecasting daily reservoir inflows

Neural networks for forecasting daily reservoir inflows

... dam reservoir requires that all components of the water resource system be ...daily reservoir inflow which is the subject matter of this study, ...Daily reservoir inflow data were extracted by ...

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Echo State Property of Deep Reservoir Computing Networks

Echo State Property of Deep Reservoir Computing Networks

... RC networks, the results of our analysis provide also insights on the nature of the state dynamics in hierarchically organized recurrent ...deep reservoir architecture, the regime of network’s dynamics can ...

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Computing with noise in spiking neural networks

Computing with noise in spiking neural networks

... spiking networks with LIF-based Boltzmann machines. Even for large-scale networks, the performance of the LIF-based BMs can be esti- ...500-neuron networks for the DKL, we evaluated the marginalized ...

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Inflow forecasting using Artificial Neural Networks for reservoir operation

Inflow forecasting using Artificial Neural Networks for reservoir operation

... Ubonratana reservoir, ...on reservoir operation performance, four situations were considered for the one-month-ahead inflow: (1) inflow is known and assumed to be the historic (Type A); (2) inflow is known ...

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Parallel Computing and Neural Networks in Behavioral Modeling

Parallel Computing and Neural Networks in Behavioral Modeling

... the neural network which is able to compute the model of general nonlinear ...regression neural network (GRNN) with additional fuzzy ...neuro-fuzzy networks were not able to deal with more than one ...

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The Use of Neural Networks in the Cloud Computing Environment

The Use of Neural Networks in the Cloud Computing Environment

... fact neural networks have been designed in an attempt to simulate the human brain in ways of implementing the tasks or functions specified is the most important advantages are the nature of the adjustment ...

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Differentiable reservoir computing

Differentiable reservoir computing

... memory reservoir filter with respect to a weighted norm admits a Volterra series representation with semi-infinite ...a reservoir filter with linear readouts and where the reservoir map has been ...

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Reservoir Computing in Materio

Reservoir Computing in Materio

... These networks attempt to mimic the vast complexity, emergent dynam- ics, and connectivity of the ...Highly-interconnected networks are constructed by bottom-up self-assembly of silver ...random ...

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Production Forecasting of Petroleum Reservoir applying Higher Order Neural Networks (HONN) with Limited Reservoir Data

Production Forecasting of Petroleum Reservoir applying Higher Order Neural Networks (HONN) with Limited Reservoir Data

... forecasting, reservoir performance, higher-order neural network, higher-order synaptic ...petroleum reservoir engineering is concerned with the forecasting of oil production from the ...the ...

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