This book introduces 'functional networks', a novel neural-based paradigm, and shows that functional network architectures can be efficiently applied to solve many interesting practical problems. Included is an introduction to neural networks, a description of functional networks, examples of applications, and computer programs in Mathematica and Java languages implementing the various algorithms and methodologies. Special emphasis is given to applications in several areas such as: * Box-Jenkins AR(p), MA(q), ARMA(p, q), and ARIMA (p, d, q) models with application to real-life economic problems such as the consumer price index, electric power consumption and international airlines' passenger data. Random time series and chaotic series are considered in relation to the Henon, Lozi, Holmes and Burger maps, as well as the problems of noise reduction and information masking. * Learning differential equations from data and deriving the corresponding equivalent difference and functional equations. Examples of a mass supported by two springs and a viscous damper or dashpot, and a loaded beam, are used to illustrate the concepts.* The problem of obtaining the most general family of implicit, explicit and parametric surfaces as used in Computer Aided Design (CAD). * Applications of functional networks to obtain general nonlinear regression models are given and compared with standard techniques. Functional Networks with Applications: A Neural-Based Paradigm will be of interest to individuals who work in computer science, physics, engineering, applied mathematics, statistics, economics, and other neural networks and data analysis related fiel

This book introduces functional networks', a novel neural-based paradigm, and shows that functional network architectures can be efficiently applied to solve many interesting practical problems. Included is an introduction to neural networks, a description of functional networks, examples of applications, and computer programs in Mathematica and Java languages implementing the various algorithms and methodologies. Special emphasis is given to applications in several areas such as: Box-Jenkins AR(p), MA(q), ARMA(p, q), and ARIMA (p, d, q) models with application to real-life economic problems such as the consumer price index, electric power consumption and international airlines' passenger data. Random time series and chaotic series are considered in relation to the HA(c)non, Lozi, Holmes and Burger maps, as well as the problems of noise reduction and information masking. Learning differential equations from data and deriving the corresponding equivalent difference and functional equations. Examples of a mass supported by two springs and a viscous damper or dashpot, and a loaded beam, are used to illustrate the concepts. The problem of obtaining the most general family of implicit, explicit and parametric surfaces as used in Computer Aided Design (CAD). Applications of functional networks to obtain general nonlinear regression models are given and compared with standard techniques. Functional Networks with Applications: A Neural-Based Paradigm will be of interest to individuals who work in computer science, physics, engineering, applied mathematics, statistics, economics, and other neural networks and data analysis related fields.

El sindrome metabolico es uno de los retos mas intrigantes y fascinantes de la medicina contemporanea. Se trata de una patologia en la que confluyen problemas de distintas areas de la medicina: diferentes grados de hipertension, acumulacion de grasa, insulinoresistencia, estados protrombotico y proinflamatorio, todos juntos en la misma persona. Esta concurrencia de factores hace que la persona tenga un riesgo mucho mayor de sufrir enfermedad cardiovascular o diabetes mellitus tipo 2, que lo que la suma de los factores individuales supondria.