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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part;

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Multi model Jumping Systems Robust Filtering and Fault Detection

as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The;

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Modelling and Estimation Strategies for Fault Diagnosis of Non-Linear Systems

This monograph presents a variety of techniques that can be used for designing robust fault diagnosis schemes for non-linear systems. The;

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New Developments in Computer Networks

cluster; fuzzy-based controller for improvement of network performance; multi-layer perceptron artificial neural network; fault detection and;

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Methodologies Of Using Neural Network And Fuzzy Logic Technologies For Motor Incipient Fault Detection

the relevant information for accurate and reliable motor fault detection. This book will introduce the neccessary concepts of neural network;

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Advanced methods for fault diagnosis and fault-tolerant control

The major objective of this book is to introduce advanced design and (online) optimization methods for fault diagnosis and fault-tolerant;

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Model-Based Fault Diagnosis Techniques

systems with successively increasing complexity. As a key technology in the search for a solution, advanced fault detection and identification;

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Fault Diagnosis and Sustainable Control of Wind Turbines

of reliable and robust fault diagnosis and fault-tolerant ('sustainable') control schemes by means of data-driven and model-based approaches. These;

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Fault Recognition in a Four Stroke Internal Combustion (IC) Engine. an Artificial Neural Network (Ann) Based Approach

Fault Recognition in a Four Stroke Internal Combustion (IC) Engine. an Artificial Neural Network (Ann) Based Approach is een boek;

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Solving Fault Diagnosis Problems

diagnosis on the basis of linear models are well understood, most of the computational methods proposed for the synthesis of fault detection and;

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Automated EEG-Based Diagnosis of Neurological Disorders

presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis;

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Automated EEG-Based Diagnosis of Neurological Disorders

presents a research ideology, a novel multi-paradigm methodology, and advanced computational models for the automated EEG-based diagnosis;

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Application of Signal Processing Tools and Artificial Neural Network in Diagnosis of Power System Faults

systems. Application of Signal Processing Tools and Neural Network in Diagnosis of Power System Faults examines power system faults and;

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Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems

, and control methods and introduces advanced data-driven schemes for the design of fault diagnosis and fault-tolerant control systems catering;

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Deep Learning Neural Networks

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving;

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Deep Learning Neural Networks

Deep Learning Neural Networks is the fastest growing field in machine learning. It serves as a powerful computational tool for solving;

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Artificial Neural Networks

, medical image fusion based on pulse coupled neural networks, artificial neural networks for intrusion detection, recognition of Arabic sign;

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Electric Machines

method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid;

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Electric Machines

method, the authors discuss robust signal processing techniques and the implementation of reference-frame-theory-based fault diagnosis for hybrid;

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Robust Discrete-Time Flight Control of UAV with External Disturbances

proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network;

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Robust Discrete Time Flight Control of UAV with External Disturbances

proposed integer-order discrete-time control schemes are based on the designed discrete-time disturbance observers (DTDOs) and the neural network;

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Bayesian Networks In Fault Diagnosis

Fault diagnosis is useful for technicians to detect, isolate, identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic;

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Fault Diagnosis for Robust Inverter Power Drives

financing. This book explores fault diagnosis of inverter drives to enable early diagnosis and robust design for efficient long life operation;

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Neural Network-Based Adaptive Control of Uncertain Nonlinear Systems

network-based control schemes. Each chapter starts with a concise introduction to the problem under study, and a neural network-based control;

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Transactions on Computational Science XIII

focus on neural-network-based trajectory prediction, privacy in vehicular ad-hoc networks, augmented reality for museum display and the consumer;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for;

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Advanced and Intelligent Computations in Diagnosis and Control

research and the development of innovative approaches to fault diagnosis. The contributions combine domains of engineering knowledge for diagnosis;

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