Classification, parameter estimation, and state estimation : an engineering approach using MATLAB

Classification, parameter estimation, and state estimation an engineering approach using MATLAB

Detalles Bibliográficos
Autor principal: Heijden, Ferdinand van der (-)
Otros Autores: Lei, Bangjun, 1973- author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Hoboken, New Jersey : Wiley 2017.
Edición:Second edition
Materias:
MATLAB.
Engineering mathematics > Data processing.
Measurement > Data processing.
Estimation theory > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009849122306719
Tabla de Contenidos:
  • Intro
  • Classification, Parameter Estimation and State Estimation
  • Contents
  • Preface
  • Acknowledgements
  • About the Companion Website
  • 1 Introduction
  • 1.1 The Scope of the Book
  • 1.1.1 Classification
  • 1.1.2 Parameter Estimation
  • 1.1.3 State Estimation
  • 1.1.4 Relations between the Subjects
  • 1.2 Engineering
  • 1.3 The Organization of the Book
  • 1.4 Changes from First Edition
  • 1.5 References
  • 2 PRTools Introduction
  • 2.1 Motivation
  • 2.2 Essential Concepts
  • 2.3 PRTools Organization Structure and Implementation
  • 2.4 Some Details about PRTools
  • 2.4.1 Datasets
  • 2.4.2 Datafiles
  • 2.4.3 Datafiles Help Information
  • 2.4.4 Classifiers and Mappings
  • 2.4.5 Mappings Help Information
  • 2.4.6 How to Write Your Own Mapping
  • 2.5 Selected Bibliography
  • 3 Detection and Classification
  • 3.1 Bayesian Classification
  • 3.1.1 Uniform Cost Function and Minimum Error Rate
  • 3.1.2 Normal Distributed Measurements
  • Linear and Quadratic Classifiers
  • 3.2 Rejection
  • 3.2.1 Minimum Error Rate Classification with Reject Option
  • 3.3 Detection: The Two-Class Case
  • 3.4 Selected Bibliography
  • 4 Parameter Estimation
  • 4.1 Bayesian Estimation
  • 4.1.1 MMSE Estimation
  • 4.1.2 MAP Estimation
  • 4.1.3 The Gaussian Case with Linear Sensors
  • 4.1.4 Maximum Likelihood Estimation
  • 4.1.5 Unbiased Linear MMSE Estimation
  • 4.2 Performance Estimators
  • 4.2.1 Bias and Covariance
  • 4.2.2 The Error Covariance of the Unbiased Linear MMSE Estimator
  • 4.3 Data Fitting
  • 4.3.1 Least Squares Fitting
  • 4.3.2 Fitting Using a Robust Error Norm
  • 4.3.3 Regression
  • 4.4 Overview of the Family of Estimators
  • 4.5 Selected Bibliography
  • 5 State Estimation
  • 5.1 A General Framework for Online Estimation
  • 5.1.1 Models
  • 5.1.2 Optimal Online Estimation
  • 5.2 Infinite Discrete-Time State Variables.
  • 5.2.1 Optimal Online Estimation in Linear-Gaussian Systems
  • 5.2.2 Suboptimal Solutions for Non-linear Systems
  • 5.3 Finite Discrete-Time State Variables
  • 5.3.1 Hidden Markov Models
  • 5.3.2 Online State Estimation
  • 5.3.3 Offline State Estimation
  • 5.4 Mixed States and the Particle Filter
  • 5.4.1 Importance Sampling
  • 5.4.2 Resampling by Selection
  • 5.4.3 The Condensation Algorithm
  • 5.5 Genetic State Estimation
  • 5.5.1 The Genetic Algorithm
  • 5.5.2 Genetic State Estimation
  • 5.5.3 Computational Issues
  • 5.6 State Estimation in Practice
  • 5.6.1 System Identification
  • 5.6.2 Observability, Controllability and Stability
  • 5.6.3 Computational Issues
  • 5.6.4 Consistency Checks
  • 5.7 Selected Bibliography
  • 6 Supervised Learning
  • 6.1 Training Sets
  • 6.2 Parametric Learning
  • 6.2.1 Gaussian Distribution, Mean Unknown
  • 6.2.2 Gaussian Distribution, Covariance Matrix Unknown
  • 6.2.3 Gaussian Distribution, Mean and Covariance Matrix Both Unknown
  • 6.2.4 Estimation of the Prior Probabilities
  • 6.2.5 Binary Measurements
  • 6.3 Non-parametric Learning
  • 6.3.1 Parzen Estimation and Histogramming
  • 6.3.2 Nearest Neighbour Classification
  • 6.3.3 Linear Discriminant Functions
  • 6.3.4 The Support Vector Classifier
  • 6.3.5 The Feedforward Neural Network
  • 6.4 Adaptive Boosting - Adaboost
  • 6.5 Convolutional Neural Networks (CNNs)
  • 6.5.1 Convolutional Neural Network Structure
  • 6.5.2 Computation and Training of CNNs
  • 6.6 Empirical Evaluation
  • 6.7 Selected Bibliography
  • 7 Feature Extraction and Selection
  • 7.1 Criteria for Selection and Extraction
  • 7.1.1 Interclass/Intraclass Distance
  • 7.1.2 Chernoff-Bhattacharyya Distance
  • 7.1.3 Other Criteria
  • 7.2 Feature Selection
  • 7.2.1 Branch-and-Bound
  • 7.2.2 Suboptimal Search
  • 7.2.3 Several New Methods of Feature Selection
  • 7.2.4 Implementation Issues.
  • 7.3 Linear Feature Extraction
  • 7.3.1 Feature Extraction Based on the Bhattacharyya Distance with Gaussian Distributions
  • 7.3.2 Feature Extraction Based on InterIntra Class Distance
  • 7.4 References
  • 8 Unsupervised Learning
  • 8.1 Feature Reduction
  • 8.1.1 Principal Component Analysis
  • 8.1.2 Multidimensional Scaling
  • 8.1.3 Kernel Principal Component Analysis
  • 8.2 Clustering
  • 8.2.1 Hierarchical Clustering
  • 8.2.2 K-Means Clustering
  • 8.2.3 Mixture of Gaussians
  • 8.2.4 Mixture of probabilistic PCA
  • 8.2.5 Self-Organizing Maps
  • 8.2.6 Generative Topographic Mapping
  • 8.3 References
  • 9 Worked Out Examples
  • 9.1 Example on Image Classification with PRTools
  • 9.1.1 Example on Image Classification
  • 9.1.2 Example on Face Classification
  • 9.1.3 Example on Silhouette Classification
  • 9.2 Boston Housing Classification Problem
  • 9.2.1 Dataset Description
  • 9.2.2 Simple Classification Methods
  • 9.2.3 Feature Extraction
  • 9.2.4 Feature Selection
  • 9.2.5 Complex Classifiers
  • 9.2.6 Conclusions
  • 9.3 Time-of-Flight Estimation of an Acoustic Tone Burst
  • 9.3.1 Models of the Observed Waveform
  • 9.3.2 Heuristic Methods for Determining the ToF
  • 9.3.3 Curve Fitting
  • 9.3.4 Matched Filtering
  • 9.3.5 ML Estimation Using Covariance Models for the Reflections
  • 9.3.6 Optimization and Evaluation
  • 9.4 Online Level Estimation in a Hydraulic System
  • 9.4.1 Linearized Kalman Filte
  • 9.4.2 Extended Kalman Filtering
  • 9.4.3 Particle Filtering
  • 9.4.4 Discussion
  • 9.5 References
  • Appendix A Topics Selected from Functional Analysis
  • A.1 Linear Spaces
  • A.1.1 Normed Linear Spaces
  • A.1.2 Euclidean Spaces or Inner Product Spaces
  • A.2 Metric Spaces
  • A.3 Orthonormal Systems and Fourier Series
  • A.4 Linear Operators
  • A.5 Selected Bibliography
  • Appendix B Topics Selected from Linear Algebra and Matrix Theory.
  • B.1 Vectors and Matrices
  • B.2 Convolution
  • B.3 Trace and Determinant
  • B.4 Differentiation of Vector and Matrix Functions
  • B.5 Diagonalization of Self-Adjoint Matrices
  • B.6 Singular Value Decomposition (SVD)
  • B.7 Selected Bibliography
  • Appendix C Probability Theory
  • C.1 Probability Theory and Random Variables
  • C.1.1 Moments
  • C.1.2 Poisson Distribution
  • C.1.3 Binomial Distribution
  • C.1.4 Normal Distribution
  • C.1.5 The Chi-Square Distribution
  • C.2 Bivariate Random Variables
  • C.3 Random Vectors
  • C.3.1 Linear Operations on Gaussian Random Vectors
  • C.3.2 Decorrelation
  • C.4 Selected Bibliography
  • Appendix D Discrete-Time Dynamic Systems
  • D.1 Discrete-Time Dynamic Systems
  • D.2 Linear Systems
  • D.3 Linear Time-Invariant Systems
  • D.3.1 Diagonalization of a System
  • D.3.2 Stability
  • D.4 Selected Bibliography
  • Index
  • EULA.

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