29 citations · 58 across the 16 of their papers we have counts for
6 papers · 1 filter
Re-visiting Reservoir Computing architectures optimized by Evolutionary Algorithms
Sebastián Basterrech, Tarun Kumar Sharma
For many years, Evolutionary Algorithms (EAs) have been applied to improve Neural Networks (NNs) architectures. They have been used for solving different problems, such as training…
Evolutionary Echo State Network: evolving reservoirs in the Fourier space
Sebastian Basterrech, Gerardo Rubino
The Echo State Network (ESN) is a class of Recurrent Neural Network with a large number of hidden-hidden weights (in the so-called reservoir). Canonical ESN and its variations have…
Empirical Analysis of the Necessary and Sufficient Conditions of the Echo State Property
Sebastián Basterrech
The Echo State Network (ESN) is a specific recurrent network, which has gained popularity during the last years. The model has a recurrent network named reservoir, that is fixed du…
A Tutorial about Random Neural Networks in Supervised Learning
Sebastián Basterrech, Gerardo Rubino
Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains…
An Experimental Analysis of the Echo State Network Initialization Using the Particle Swarm Optimization
Sebastián Basterrech, Enrique Alba, Václav Snášel
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) algori…
Echo State Queueing Network: a new reservoir computing learning tool
Sebastián Basterrech, Gerardo Rubino
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…