Sara M. George – författare

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.Features:Provides comprehensive coverage of all issues related to parameter and state estimation.Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation.Explores convergence and stability results for the discussed methods.Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting.Includes MATLAB®-based illustrative examples, and exercises.This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.

Features:

Provides comprehensive coverage of all issues related to parameter and state estimation. Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation. Explores convergence and stability results for the discussed methods. Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting. Includes MATLAB®-based illustrative examples, and exercises.

This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

This book deals with the basics of parameter estimation and state estimation as the fundamental building blocks of mathematical modelling activity in the broader field of control theory. All the methods are validated using MATLAB®-based implementations with realistically simulated data for general dynamic systems, as well as for aircraft parameter estimation. This book includes several illustrative examples and chapter-end exercises.

Features:

Provides comprehensive coverage of all issues related to parameter and state estimation. Discusses advanced topics related to Kalman filter, stability analysis, image centroid tracking and neural networks for parameter estimation. Explores convergence and stability results for the discussed methods. Reviews the estimation of parameters in linear/nonlinear models, and distributed fitting. Includes MATLAB®-based illustrative examples, and exercises.

This book is aimed at researchers and graduate students in systems and control, signal processing, estimation theory, engineering mathematics, and aerospace engineering.

Intelligent Estimation: A Soft Computing Paradigm presents a unified treatment of a new soft computing framework using AI-based approaches for system identification, parameter estimation, and filtering. This Neuro-Fuzzy-GA-based methodology, succinctly referred to as Intelligent Estimation (IE), integrates these estimation and filtering tasks within a single paradigm.Offering a thorough understanding of soft computing-based estimation concepts and theory, the book discusses a modelling-control-system approach with numerous practical applications in solving mathematical modelling problems for industrial and aerospace engineering systems. It delves into theory, concepts, and various ramifications of neural networks, fuzzy logic, and genetic algorithms for modelling, system identification, filtering, and estimation.This book is intended for upper-level undergraduate and graduate engineering students studying soft computing, intelligent systems, and advanced control systems in industry applications.Instructors will be able to utilize a solutions manual and figure slides for their course.

Intelligent Estimation: A Soft Computing Paradigm presents a unified treatment of a new soft computing framework using AI-based approaches for system identification, parameter estimation, and filtering. This Neuro-Fuzzy-GA-based methodology, succinctly referred to as Intelligent Estimation (IE), integrates these estimation and filtering tasks within a single paradigm.Offering a thorough understanding of soft computing-based estimation concepts and theory, the book discusses a modelling-control-system approach with numerous practical applications in solving mathematical modelling problems for industrial and aerospace engineering systems. It delves into theory, concepts, and various ramifications of neural networks, fuzzy logic, and genetic algorithms for modelling, system identification, filtering, and estimation.This book is intended for upper-level undergraduate and graduate engineering students studying soft computing, intelligent systems, and advanced control systems in industry applications.Instructors will be able to utilize a solutions manual and figure slides for their course.

Intelligent Estimation: A Soft Computing Paradigm presents a unified treatment of a new soft computing framework using AI-based approaches for system identification, parameter estimation, and filtering. This Neuro-Fuzzy-GA-based methodology, succinctly referred to as Intelligent Estimation (IE), integrates these estimation and filtering tasks within a single paradigm.Offering a thorough understanding of soft computing-based estimation concepts and theory, the book discusses a modelling-control-system approach with numerous practical applications in solving mathematical modelling problems for industrial and aerospace engineering systems. It delves into theory, concepts, and various ramifications of neural networks, fuzzy logic, and genetic algorithms for modelling, system identification, filtering, and estimation.This book is intended for upper-level undergraduate and graduate engineering students studying soft computing, intelligent systems, and advanced control systems in industry applications.Instructors will be able to utilize a solutions manual and figure slides for their course.