Time series modeling and synchronization using neural networks

In the last few years, neural networks have found interesting applications in the field of time series modeling and forecasting. Some recent results show the ability of these models to approximate the dynamical behavior of nonlinear chaotic systems, leading to similar dimensions and Lyapunov exponents. In this paper we analyze further the dynamical properties of neural networks when comparted with chaotic systems. In particular, we show that the possibility of synchronizing chaotic systems gives a natural criterion for determining similar dynamical behavior between these systems and neural approximate models. In particular we show that a neural model obtained from an experimental scalar laser-intensity time series can be synchronized to the time series, indicating that it captures the dynamical behavior of the system underlying the data.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem

The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paperI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Minimum description length quality measurues for modular functional network architectures

Modular neural networks (MNNs) are increasingly popular models for dealing with complex problems constituted by a number of dependent subtasks. An important problem on MNNs is finding the optimal aggregation of the neural modules, each of them dealing with one of the subproblems. In this paper, we present a functional network approach, based on the minimum description length quality measure, to the problem of finding optimal modular network architectures for specific problems. Examples of function approximation and nonlinear time series prediction are used to illustrate the performance of these models when compared with standard functional and neural networks.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

A comparison of different evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem

The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations)which approximates a given image with a certain prescribed accuracy (inverse IFS problem).In this paper,we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method.We also present an nteractive Matlab program implementing the algorithms described in the paper.The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations)which approximates a given image with a certain prescribed accuracy (inverse IFS problem).In this paper,we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method.We also present an nteractive Matlab program implementing the algorithms described in the paper.Facultad de Informátic

Time series modeling and synchronization using neural networks

In the last few years, neural networks have found interesting applications in the field of time series modeling and forecasting. Some recent results show the ability of these models to approximate the dynamical behavior of nonlinear chaotic systems, leading to similar dimensions and Lyapunov exponents. In this paper we analyze further the dynamical properties of neural networks when comparted with chaotic systems. In particular, we show that the possibility of synchronizing chaotic systems gives a natural criterion for determining similar dynamical behavior between these systems and neural approximate models. In particular we show that a neural model obtained from an experimental scalar laser-intensity time series can be synchronized to the time series, indicating that it captures the dynamical behavior of the system underlying the data.I Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Minimum description length quality measurues for modular functional network architectures

Modular neural networks (MNNs) are increasingly popular models for dealing with complex problems constituted by a number of dependent subtasks. An important problem on MNNs is finding the optimal aggregation of the neural modules, each of them dealing with one of the subproblems. In this paper, we present a functional network approach, based on the minimum description length quality measure, to the problem of finding optimal modular network architectures for specific problems. Examples of function approximation and nonlinear time series prediction are used to illustrate the performance of these models when compared with standard functional and neural networks.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

A comparison of diferent evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem

The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations) which approximates a given image with a certain prescribed accuracy (inverse IFS problem). In this paper, we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method. We also present an interactive Matlab program implementing the algorithms described in the paperI Workshop de Agentes y Sistemas Inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

A comparison of different evolutive niching strategies for identifying a set of selfsimilar contractions for the IFS inverse problem

The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations)which approximates a given image with a certain prescribed accuracy (inverse IFS problem).In this paper,we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method.We also present an nteractive Matlab program implementing the algorithms described in the paper.The key problem in fractal image compression is that of obtaining the IFS code (a set of linear transformations)which approximates a given image with a certain prescribed accuracy (inverse IFS problem).In this paper,we analyze and compare the performance of sharing and crowding niching techniques for identifying optimal selfsimilar transformations likely to represent a selfsimilar area within the image. The best results are found using the deterministic crowding method.We also present an nteractive Matlab program implementing the algorithms described in the paper.Facultad de Informátic

Redes neuronales y patrones de analogías aplicados al downscaling en modelos climáticos

Ponencia presentada en: I Congreso de la Asociación Española de Climatología “La climatología española en los albores del siglo XXI”, celebrado en Barcelona del 1 al 3 de diciembre de 1999.[ES]Este artículo describe un sistema experto para la simulación climática local utilizando las salidas de los modelos climáticos sobre un área limitada supra-peninsular y buscando analogías en las bases de datos de cada modelo. Éste conjunto de analogías entrena a una red neuronal sobre los datos locales de cualquier observatorio resultando un sistema objetivo para interpretar localmente las salidas de los modelos climáticos.[EN]This article describes an expert system for the climatic simulation using the low resolution outputs of the climatic models ver a supra-peninsular limited area and looking for similar configurations in the data bases of each model. These allow to train a neural net using the local data of any observatory as output. It provides an objective system in order to interpret the outputs of the climatic models locally

Desarrollo y análisis de una rejilla de observaciones de alta resolución sobre España, a escala diaria

Póster presentado en: XXX Jornadas Científicas de la AME y el IX Encuentro Hispano Luso de Meteorología celebrado en Zaragoza, del 5 al 7 de mayo de 2008