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Neural Network-Based State Estimation of Nonlinear Systems

Neural Network-Based State Estimation of Nonlinear Systems PDF Author: Heidar A. Talebi
Publisher: Springer
ISBN: 1441914382
Category : Technology & Engineering
Languages : en
Pages : 154

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Book Description
"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Neural Network-Based State Estimation of Nonlinear Systems

Neural Network-Based State Estimation of Nonlinear Systems PDF Author: Heidar A. Talebi
Publisher: Springer
ISBN: 1441914382
Category : Technology & Engineering
Languages : en
Pages : 154

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Book Description
"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Neural Network-Based State Estimation of Nonlinear Systems

Neural Network-Based State Estimation of Nonlinear Systems PDF Author: Heidar A. Talebi
Publisher: Springer
ISBN: 9781441914446
Category : Technology & Engineering
Languages : en
Pages : 154

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Book Description
"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems PDF Author: Kasra Esfandiari
Publisher: Springer Nature
ISBN: 3030731367
Category : Technology & Engineering
Languages : en
Pages : 163

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Book Description
The focus of this book is the application of artificial neural networks in uncertain dynamical systems. It explains how to use neural networks in concert with adaptive techniques for system identification, state estimation, and control problems. The authors begin with a brief historical overview of adaptive control, followed by a review of mathematical preliminaries. In the subsequent chapters, they present several neural network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control strategy is designed for the simplest case scenario. After these designs are discussed, different practical limitations (i.e., saturation constraints and unavailability of all system states) are gradually added, and other control schemes are developed based on the primary scenario. Through these exercises, the authors present structures that not only provide mathematical tools for navigating control problems, but also supply solutions that are pertinent to real-life systems.

Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach

Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach PDF Author: Ehsan Sobhani-Tehrani
Publisher: Springer
ISBN: 038792907X
Category : Technology & Engineering
Languages : en
Pages : 268

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Book Description
Theincreasingcomplexityofspacevehiclessuchassatellites,andthecostreduction measures that have affected satellite operators are increasingly driving the need for more autonomy in satellite diagnostics and control systems. Current methods for detecting and correcting anomalies onboard the spacecraft as well as on the ground are primarily manual and labor intensive, and therefore, tend to be slow. Operators inspect telemetry data to determine the current satellite health. They use various statisticaltechniques andmodels,buttheanalysisandevaluation ofthelargevolume of data still require extensive human intervention and expertise that is prone to error. Furthermore, for spacecraft and most of these satellites, there can be potentially unduly long delays in round-trip communications between the ground station and the satellite. In this context, it is desirable to have onboard fault-diagnosis system that is capable of detecting, isolating, identifying or classifying faults in the system withouttheinvolvementandinterventionofoperators.Towardthisend,theprinciple goal here is to improve the ef?ciency, accuracy, and reliability of the trend analysis and diagnostics techniques through utilization of intelligent-based and hybrid-based methodologies.

Artificial Higher Order Neural Networks for Modeling and Simulation

Artificial Higher Order Neural Networks for Modeling and Simulation PDF Author: Zhang, Ming
Publisher: IGI Global
ISBN: 1466621761
Category : Computers
Languages : en
Pages : 454

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Book Description
"This book introduces Higher Order Neural Networks (HONNs) to computer scientists and computer engineers as an open box neural networks tool when compared to traditional artificial neural networks"--Provided by publisher.

Discrete-Time Neural Observers

Discrete-Time Neural Observers PDF Author: Alma Y. Alanis
Publisher: Academic Press
ISBN: 0128105445
Category : Computers
Languages : en
Pages : 150

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Book Description
Discrete-Time Neural Observers: Analysis and Applications presents recent advances in the theory of neural state estimation for discrete-time unknown nonlinear systems with multiple inputs and outputs. The book includes rigorous mathematical analyses, based on the Lyapunov approach, that guarantee their properties. In addition, for each chapter, simulation results are included to verify the successful performance of the corresponding proposed schemes. In order to complete the treatment of these schemes, the authors also present simulation and experimental results related to their application in meaningful areas, such as electric three phase induction motors and anaerobic process, which show the applicability of such designs. The proposed schemes can be employed for different applications beyond those presented. The book presents solutions for the state estimation problem of unknown nonlinear systems based on two schemes. For the first one, a full state estimation problem is considered; the second one considers the reduced order case with, and without, the presence of unknown delays. Both schemes are developed in discrete-time using recurrent high order neural networks in order to design the neural observers, and the online training of the respective neural networks is performed by Kalman Filtering. Presents online learning for Recurrent High Order Neural Networks (RHONN) using the Extended Kalman Filter (EKF) algorithm Contains full and reduced order neural observers for discrete-time unknown nonlinear systems, with and without delays Includes rigorous analyses of the proposed schemes, including the nonlinear system, the respective observer, and the Kalman filter learning Covers real-time implementation and simulation results for all the proposed schemes to meaningful applications

Differential Neural Networks for Robust Nonlinear Control

Differential Neural Networks for Robust Nonlinear Control PDF Author: Alexander S Poznyak
Publisher: World Scientific
ISBN: 9814491020
Category : Computers
Languages : en
Pages : 456

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Book Description
This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents:Theoretical Study:Neural Networks StructuresNonlinear System Identification: Differential LearningSliding Mode Identification: Algebraic LearningNeural State EstimationPassivation via Neuro ControlNeuro Trajectory TrackingNeurocontrol Applications:Neural Control for ChaosNeuro Control for Robot ManipulatorsIdentification of Chemical ProcessesNeuro Control for Distillation ColumnGeneral Conclusions and Future WorkAppendices:Some Useful Mathematical FactsElements of Qualitative Theory of ODELocally Optimal Control and Optimization Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks. Keywords:Dynamic Neural Networks;System Identification;State Estimation;Adaptive Control;Robust Control;Sliding Mode;Chaos Identification and Control;Chemical Process;Lyapunov Method;StabilityReviews:“This book is the result of many years of research and publications by the authors. Overall, it is a good one that could benefit the researchers and practitioners in the field of intelligent nonlinear control systems. Design methods and analytical results are well presented and substantiated by closely-related simulation examples and engineering applications. It is a very good addition to the libraries of those interested in the subject. It is also qualified to be used as a postgraduate-level reference.”International Journal of Adaptive Control and Signal Processing

Network and Communication Technology Innovations for Web and IT Advancement

Network and Communication Technology Innovations for Web and IT Advancement PDF Author: Alkhatib, Ghazi I.
Publisher: IGI Global
ISBN: 1466621583
Category : Computers
Languages : en
Pages : 355

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Book Description
With the steady stream of new web based information technologies being introduced to organizations, the need for network and communication technologies to provide an easy integration of knowledge and information sharing is essential. Network and Communication Technology Innovations for Web and IT Advancement presents studies on trends, developments, and methods on information technology advancements through network and communication technology. This collection brings together integrated approaches for communication technology and usage for web and IT advancements.

Block-oriented Nonlinear System Identification

Block-oriented Nonlinear System Identification PDF Author: Fouad Giri
Publisher: Springer
ISBN: 1849965137
Category : Technology & Engineering
Languages : en
Pages : 426

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Book Description
Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: iterative and over-parameterization techniques; stochastic and frequency approaches; support-vector-machine, subspace, and separable-least-squares methods; blind identification method; bounded-error method; and decoupling inputs approach. The identification methods are presented by authors who have either invented them or contributed significantly to their development. All the important issues e.g., input design, persistent excitation, and consistency analysis, are discussed. The practical relevance of block-oriented models is illustrated through biomedical/physiological system modelling. The book will be of major interest to all those who are concerned with nonlinear system identification whatever their activity areas. This is particularly the case for educators in electrical, mechanical, chemical and biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Block-oriented Nonlinear System Identification serves as a reference for active researchers, new comers, industrial and education practitioners and graduate students alike.

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks

Emerging Capabilities and Applications of Artificial Higher Order Neural Networks PDF Author: Zhang, Ming
Publisher: IGI Global
ISBN: 1799835650
Category : Computers
Languages : en
Pages : 540

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Book Description
Artificial neural network research is one of the new directions for new generation computers. Current research suggests that open box artificial higher order neural networks (HONNs) play an important role in this new direction. HONNs will challenge traditional artificial neural network products and change the research methodology that people are currently using in control and recognition areas for the control signal generating, pattern recognition, nonlinear recognition, classification, and prediction. Since HONNs are open box models, they can be easily accepted and used by individuals working in information science, information technology, management, economics, and business fields. Emerging Capabilities and Applications of Artificial Higher Order Neural Networks contains innovative research on how to use HONNs in control and recognition areas and explains why HONNs can approximate any nonlinear data to any degree of accuracy, their ease of use, and how they can have better nonlinear data recognition accuracy than SAS nonlinear procedures. Featuring coverage on a broad range of topics such as nonlinear regression, pattern recognition, and data prediction, this book is ideally designed for data analysists, IT specialists, engineers, researchers, academics, students, and professionals working in the fields of economics, business, modeling, simulation, control, recognition, computer science, and engineering research.