The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in financial systems when these are described in the form of nonlinear ordinary differential equations. It then addresses problems associated with the control and estimation of financial systems governed by partial differential equations (e.g. the Black-Scholes partial differential equation (PDE) and its variants). Lastly it an offers optimal solution to the problem of statistical validation of computational models and tools used to support financial engineers in decision making. The application of state-space models in financial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for finance can be eliminated. It also allows methods of fault-free performance and optimality in the management of assets and capitals and methods assuring stability in the functioning of financial systems to be established. Covering the following key areas of financial engineering: (i) control and stabilization of financial systems dynamics, (ii) state estimation and forecasting, and (iii) statistical validation of decision-making tools, the book can be used for teaching undergraduate or postgraduate courses in financial engineering. It is also a useful resource for the engineering and computer science community

This monograph presents recent advances in differential flatness theory and analyzes its use for nonlinear control and estimation. It shows how differential flatness theory can provide solutions to complicated control problems, such as those appearing in highly nonlinear multivariable systems and distributed-parameter systems. Furthermore, it shows that differential flatness theory makes it possible to perform filtering and state estimation for a wide class of nonlinear dynamical systems and provides several descriptive test cases. The book focuses on the design of nonlinear adaptive controllers and nonlinear filters, using exact linearization based on differential flatness theory. The adaptive controllers obtained can be applied to a wide class of nonlinear systems with unknown dynamics, and assure reliable functioning of the control loop under uncertainty and varying operating conditions. The filters obtained outperform other nonlinear filters in terms of accuracy of estimation and computation speed. The book presents a series of application examples to confirm the efficiency of the proposed nonlinear filtering and adaptive control schemes for various electromechanical systems. These include: * industrial robots; * mobile robots and autonomous vehicles; * electric power generation; * electric motors and actuators; * power electronics;* internal combustion engines; * distributed-parameter systems; and * communication systems. Differential Flatness Approaches to Nonlinear Control and Filtering will be a useful reference for academic researchers studying advanced problems in nonlinear control and nonlinear dynamics, and for engineers working on control applications in electromechanical systems.

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in financial systems when these are described in the form of nonlinear ordinary differential equations. It then addresses problems associated with the control and estimation of financial systems governed by partial differential equations (e.g. the Black-Scholes partial differential equation (PDE) and its variants). Lastly it an offers optimal solution to the problem of statistical validation of computational models and tools used to support financial engineers in decision making. The application of state-space models in financial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for finance can be eliminated. It also allows methods of fault-free performance and optimality in the management of assets and capitals and methods assuring stability in the functioning of financial systems to be established. Covering the following key areas of financial engineering: (i) control and stabilization of financial systems dynamics, (ii) state estimation and forecasting, and (iii) statistical validation of decision-making tools, the book can be used for teaching undergraduate or postgraduate courses in financial engineering. It is also a useful resource for the engineering and computer science community

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

Fault detection and isolation is an important topic for researchers in the area of robotics and for industrial systems engineers. The need for a systematic method that will permit preventive maintenance through the diagnosis of incipient faults is obvious. At the same time it is desirable to reduce the false alarms rate so as to avoid unnecessary and costly interruptions of industrial processes and robotic tasks. The proposed book aims at analyzing recent advances in the area of fault diagnosis for robotic and industrial systems. There are totally 9 chapters in this book. Chapter 1 deals with supervision for the safe navigation of autonomous robots in a natural environment. Fault diagnosis and natural environment perception are used at different levels within a supervisor architecture and real operation is demonstrated on an autonomous tractor driving in an orchard. Chapter 2 gives an introduction to fault tolerant sensor systems which is based on the Failure Modes and Effects Analysis (FMEA) method. Chapter 3 aims at analyzing and implementing new solutions for the problem of distributed estimation for condition monitoring of nonlinear dynamical systems (e.g. automatic ground vehicles, unmanned surface or underwater vessels and unmanned aerial vehicles), so as to enable early detection of faults and the take up of efficient restoration measures. To this end, the development of distributed nonlinear state estimation and distributed fault detection and isolation (FDI) tools is proposed. Chapter 4 proposes so-called logic-dynamic approach for fault diagnosis in industrial systems described by nonlinear dynamic models with non-differentiable nonlinearities. The approach allows solving the problem of fault diagnosis is nonlinear systems using well-known linear methods. In Chapter 5, observer design for nonlinear systems described by a Takagi-Sugeno model with unmeasurable premise variables is proposed. Furthermore, a fault tolerant controller is proposed for such a system in order to preserve some performances of the system by trajectory tracking in faulty situations. Chapter 6 explains and demonstrates the utilization of different nonlinear-dynamics-based procedures for the purposes of structural health monitoring as well as for monitoring of robot joints based on Vibration-based Health Monitoring (VHM) methods In Chapter 7, vibrations picked from spalled defective rolling element bearings is presented. It uses a four stage processing algorithm to detect and diagnose the defective component in rolling element. In Chapter 8, the problem of fault diagnosis with parity equations is considered for nonlinear dynamic systems whose models are taken in the form of ordinary differential equations. The active and passive approaches are involved to achieve the robustness of the diagnostic procedure Finally, Chapter 9 proposes a graphical method for diagnosis of nonlinear systems. The proposed method is based on a 2D signature obtained by measurements projection over some moving time-window. This projection highlights what happens inside the system and enables the diagnosis of abnormal behaviors. This book is suitable for advanced undergraduate students and postgraduate students. It takes a practical approach rather than a conceptual approach. It offers a truly reader-friendly way to get to the subject related to the semantic web, making it the ideal resources for any student who is new to this subject and providing a definitive guide to anyone in this vibrant and evolving discipline. This book is an invaluable companion for students from their first encounter with the subject to more advanced studies, while the high quality artworks are designed to present the key concepts with simplicity, clarity and consistency.