Sebastián Basterrech

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In the last years a new approach for designing and training artificial Recurrent Neural Network (RNN) have been investigated under the name of Reservoir Computing (RC). One important model in the field of RC has been developed under the name of Echo State Networks (ESNs). Traditionally, an ESN uses a RNN with random untrained parameters called the(More)
This article introduces a robust hybrid method for solving supervised learning tasks, which uses the Echo State Network (ESN) model and the Particle Swarm Optimization (PSO) algorithm. An ESN is a Recurrent Neural Network with the hidden-hidden weights fixed in the learning process. The recurrent part of the network stores the input information in internal(More)
For several years now, the ITU-T’s Perceptual Evaluation of Speech Quality (PESQ [1]) has been the reference for objective speech quality assessment. It is widely deployed in commercial QoE measurement products, and it has been well studied in the literature. While PESQ does provide reasonably good correlation with subjective scores for VoIP applications,(More)
The trend of <i>Reservoir Computing (RC)</i> has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called <i>reservoir</i>, which expands the input data and historical information into a high-dimensional space. This projection is carried(More)
Since the early 1990s, Random Neural Networks (RNNs) have gained importance in the Neural Networks and Queueing Networks communities. RNNs are inspired by biological neural networks and they are also an extension of open Jackson's networks in Queueing Theory. In 1993, a learning algorithm of gradient type was introduced in order to use RNNs in supervised(More)
In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has(More)
This paper investigates the estimation of a real time-series benchmark: the solar irradiance forcasting. The global solar irradiance is an important variable in the production of renewable energy sources. These variable is very unstable and hard to be predicted. For the prediction, we use two new models for time-series modeling: Echo State Queueing Networks(More)