Identification, Adaptation, Learning

This book offers a tutorial view of the science of learning models from data. It covers the most important approaches to linear modelling, nonlinear model construction from data, fuzzy logic based modelling, and optimization methods.

Klappentext

This book offers a tutorial view of recent trends in the science of modelling, adaptation, and learning. The most important modern approaches to identification, namely the stochastic, behavioral, subspace, and frequency domain approaches, are discussed thoroughly. On adaptation, tuning the parameters of a linear model is presented as a cure for uncertainty, and the asymptotics of recursive least squares and self-tuning systems are explained simply after a fully deterministic analysis. For constructing nonlinear models from data, neural networks and wavelets are considered as useful nonlinear tools, and fuzzy logic is presented as a way of coping with qualitative information. A final chapter deals with optimization methods. The book will become an important reference for researchers in the field.

Inhalt
Geometric Methods for State Space Identification.- Parameter Estimation of Multivariable Systems Using Balanced Realizations.- Balanced Canonical Forms.- From Data to State Model.- Identification of Linear Systems from Noisy Data.- Identification in H?: Theory and Applications.- System Identification with Information Theoretic Criteria.- Least Squares Based Self-Tuning Control Systems.- On Neural Network Model Structures in System Identification.- An Overview of Computational Learning Theory and Its Applications to Neural Network Training.- Just-in-Time Learning and Estimation.- Wavelets in Identification.- Fuzzy Logic Modelling and Control.- Searching for the Best: Stochastic Approximation, Simulated Annealing and Related Procedures.- List of Contributors.