Extracting knowledge from time series: an introduction to nonlinear empirical modeling

This book addresses the fundamental question on how to construct mathematicalmodels for the evolution of dynamical systems from experimentally obtained time series. Emphasis is on chaotic signals and nonlinear modeling, with the aimto obtain a quantitative measure for the forecast of future system evolution.In particular, the reader will learn how to construct difference and differential model equations depending on the amount of a priori information that is available on the system in addition to the experimental data sets. This book will benefit graduate students and researchers from all natural sciences alike, who seek a self-contained and thorough introduction to this subject. Useful asa self-study guide Gives a modern approach and practical examples Written by well known authors having made many contribution to the field INDICE: Introduction .- Part I: Modeling and Forecast.- The concept of mathematical modelling.- Two approaches to Modeling and Forecast.- Deterministic Models of dynamical evolution.- Stochastic Models of dynamical evolution.- Part II Modeling from Data Series.- Problem settings.- Sources of data.- Reconstruction of explicit temporal dependencies.- Model equations: parameter estimation.- Model equations : reconstruction of nonlinear characteristics.- Reconstruction of equations.- Selected empirical models.- References .- Index.

• ISBN: 978-3-642-12600-0
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 350
• Fecha Publicación: 26/09/2010
• Nº Volúmenes: 1
• Idioma: Inglés