Foreword

A broad range of contemporary engineering problems requires estimating the class (category) of a sensed object or process, parameters controlling the behavior of a “black box” system, or its internal state. The goal of many of these systems is to interact in an intelligent manner with their environment. While the technological advances in sensor design and processors have enabled development of low-cost and real-time systems, algorithms for classification and parameter estimation still need continued development in order to have a more accurate object classification and robust parameter estimation. A variety of disciplines – automatic control, signal processing, statistics, pattern recognition, machine learning – offer a spectrum of solutions to these problems, yet exhibit a convergence to several key approaches. A comprehensive treatment of these approaches is the main objective of this book.

This book emphasizes a unified mathematical treatment of model-based classification and estimation problems across different engineering applications. It provides a practical guide for implementing a wide range of algorithms for supervised and unsupervised classification, feature selection, system identification, and state estimation. The text covers both classical and state-of-the-art algorithms by utilizing MATLAB software that is routinely used in engineering design. One of the main contributions of this book, that distinguishes it from other pattern recognition books, is that ...

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