Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains

John Wiley & Sons, 2013 M07 29 - 576 páginas
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Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains describes a comprehensive framework for the identification and analysis of nonlinear dynamic systems in the time, frequency, and spatio-temporal domains. This book is written with an emphasis on making the algorithms accessible so that they can be applied and used in practice.

Includes coverage of:

  • The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model
  • The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio reveals the percentage contribution of each model term
  • Statistical and qualitative model validation methods that can be applied to any model class
  • Generalised frequency response functions which provide significant insight into nonlinear behaviours
  • A completely new class of filters that can move, split, spread, and focus energy
  • The response spectrum map and the study of sub harmonic and severely nonlinear systems
  • Algorithms that can track rapid time variation in both linear and nonlinear systems
  • The important class of spatio-temporal systems that evolve over both space and time
  • Many case study examples from modelling space weather, through identification of a model of the visual processing system of fruit flies, to tracking causality in EEG data are all included
    to demonstrate how easily the methods can be applied in practice and to show the insight that the algorithms reveal even for complex systems

NARMAX algorithms provide a fundamentally different approach to nonlinear system identification and signal processing for nonlinear systems. NARMAX methods provide models that are transparent, which can easily be analysed, and which can be used to solve real problems.

This book is intended for graduates, postgraduates and researchers in the sciences and engineering, and also for users from other fields who have collected data and who wish to identify models to help to understand the dynamics of their systems.


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Model Structure Detection and Parameter Estimation
Models for Linear and Nonlinear Systems
Feature Selection and Rankin
Model Validation
The Identification and Analysis of Nonlinear Systems
Design of Nonlinear Systems in the Frequency Domain
Neural Networks for Nonlinear System Identification
Identification of ContinuousTime Nonlinear Models
differential Equation Models Without Differentiation
TimeVarying and Nonlinear System Identification
Identification of Cellular Automata and NState Models
Identification of Coupled Map Lattice and Partial
Case Studies

Severely Nonlinear Systems

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Acerca del autor (2013)

Stephen A. Billings is Professor of Signal Processing and Complex Systems, and Director of the Signal Processing and Complex Systems Research Group, in the Department of Automatic Control and Systems Engineering at the University of Sheffield, He is counted as "highly cited" by the ISI Web of Knowledge with 250 publications to his name.

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