Search Results

This work presents a novel variant of Echo State Network (ESN) known as Time-Varying Echo State Network (TV-ESN) and conducts a comprehensive comparative analysis with the standard ESN model. TV-ESN introduces dynamic variations in the leaking rate (a) ...

Neuromorphic computing systems have emerged as powerful computation tools in the field of object recognition and control systems. However, training these systems, which are usually characterized by recurrent connectivity, requires abundant computational ...

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 ...

We introduce ReservoirComputing.jl, an open source Julia library for reservoir computing models. It is designed for temporal or sequential tasks such as time series prediction and modeling complex dynamical systems. As such it is suited to process a ...

We analyze the practices of reservoir computing in the framework of statistical learning theory. In particular, we derive finite sample upper bounds for the generalization error committed by specific families of reservoir computing systems when processing ...

Reservoir Computers such as Echo State Networks (ESN) represent an alternative recurrent neural network model that provides fast training and state-of-the-art performances for supervised learning problems. Classic ESNs suffer from two limitations; ...

A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts are causal, time-invariant, and ...

Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work ...

Echo state networks ESN are an emerging learning technique proposed for generalised single-hidden layer feed forward networks SLFNs. However, the conventional ESN ignores training data timeliness, which may reduce prediction accuracy for time varying ...

A continuous multimodal human affect recognition for both arousal and valence dimensions in a non-acted spontaneous scenario is investigated in this paper. Different regression models based on Random Forests and Echo State Networks are evaluated and ...

In this article, a study of the mean-square error (MSE) performance of linear echo-state neural networks is performed, both for training and testing tasks. Considering the realistic setting of noise present at the network nodes, we derive deterministic ...

In a human robot interaction scenario, predicting the human motion intention is essential for avoiding inconvenient delays and for a smooth reactivity of the robotic system. In particular, when dealing with hand prosthetic devices, an early estimation ...

In this paper the use of sparse linear regression algorithms in echo state networks (ESN) is presented for reducing the number of readouts and improving the robustness and generalization properties of ESNs. Three data sets with overall 80 tests are used ...

This paper explores the use of a reservoir computing (RC) method, Echo State Networks (ESN) to classify inertial sensor motion data collected from sensors worn by horse riders into punctual activities of interest within a scripted movement environment. ...

Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) represent promising alternatives in time series forecasting in view of their intrinsic trade-off between performance and mathematical tractability. Both approaches share a key feature: ...

Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. The current research provides a possible explanation of how certain aspects of this on-line language processing can occur, ...

The prediction of seasonal streamflow series is very important in countries where power generation is predominantly done by hydroelectric plants. Echo state networks can be safely regarded as promising tools in forecasting because they are recurrent ...

This paper extends a recent and very appealing approach of computational learning to the field of image analysis. Recent works have demonstrated that the implementation of Artificial Neural Networks (ANN) could be simplified by using a large amount of ...

Analog circuit fault diagnosis can be modeled as a pattern recognition problem. Fault patterns are complicated which has high demands for classification accuracy and efficiency. Therefore a new analog circuit fault diagnosis method using Echo State ...

A novel complex echo state network (ESN), utilizing full second-order statistical information in the complex domain, is introduced. This is achieved through the use of the so-called augmented complex statistics, thus making complex ESNs suitable for ...