In this expository talk, we will review the Echo State Network (ESN), a recurrent neural network that has achieved good results in time series tasks, such as forecasting, classification, and encoding-decoding. However, the ...

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The ...

Esta tesis presenta un estudio de las redes neuronales de Hopfield, en cuanto a su capacidad para resolver problemas de optimización. El análisis teórico de las características de estos sistemas se abordan con rigor ...

We propose the higher order extension of numerical methods that preserve the energy-diminishing feature of a system with Lyapunov function.

This paper proposes an adaptative control algorithm, which is designed by adding a parametric identification method to a non-linear controller. The identification module is built upon the Hopfield neural network, resulting in ...

Esta tesis estudia métodos de integración numérica geométrica, específicamente, métodos numéricos diseñados para conservar la función de Lyapunov de un sistema dinámico. En otras palabras, se proponen aproximaciones numéricas ...

A non-negative matrix factorization approach to dimensionality reduction is proposed to aid classification of images. The original images can be stored as lower-dimensional columns of a matrix that hold degrees of belonging ...

In this contribution we implement and assess numerical methods for gradient systems, i.e. dynamical systems that possess a Lyapunov function, and consequently are stable. In particular, we claim that discrete gradient ...

It is impressive when one gets to see a hundreds or thousands years old artefact exhibited in the museum, whose appearance seems to have been untouched by centuries. Its restoration had been in the hands of a multidisciplinary ...