This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

527 articles related to imbalanced data and rare events are reviewed.Viewing reviewed papers from both technical and practical perspectives.Summarizing existing methods and corresponding statistics by a new taxonomy idea.Categorizing 162 application papers into 13 domains and giving introduction.Some opening questions are discussed at the end of this manuscript. Rare events, especially those that could potentially negatively impact society, often require humans decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields.

Learning from class-imbalanced data

Feature selection is an important task in data mining and machine learning to reduce the dimensionality of the data and increase the performance of an algorithm, such as a classification algorithm. However, feature selection is a challenging task due mainly to the large search space. A variety of methods have been applied to solve feature selection problems, where evolutionary computation (EC) techniques have recently gained much attention and shown some success. However, there are no comprehensive guidelines on the strengths and weaknesses of alternative approaches. This leads to a disjointed and fragmented field with ultimately lost opportunities for improving performance and successful applications. This paper presents a comprehensive survey of the state-of-the-art work on EC for feature selection, which identifies the contributions of these different algorithms. In addition, current issues and challenges are also discussed to identify promising areas for future research.

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A Survey on Evolutionary Computation Approaches to Feature Selection

Binary grey wolf optimization approaches for feature selection

Pattern recognition

In this paper, model based fault detection of gas turbine using linear and non-linear methods (multilayer perceptron and radial basis function neural network models) is studied. We contemplate IGV positions and gas flow as input and sensors related to compressor as outputs. Then residual signals will be obtained based on system model. In addition, by these signals and exert the fixed and adaptive thresholds, the fault occurred in the V94.2 gas turbine which is pollution of vane compressor (Fouling detection) has identified and diagnosed. Consequently, by comparing the obtained results from different fault detection methods, we determine the most appropriate signal output that led to better and reliable result. All simulations have been carried out by using real data taken from an V94.2 industrial gas turbine 927 power plant in Fars.

V94.2 industrial gas turbine compressor fouling detection based on system identification methods, neural networks and experimental data

Inverted Pendulum Fault Tolerant Control Based on Fuzzy Backstepping Design and Anti-Control of Chaos

Decision-making systems are known as the main pillar of industrial alarm systems, and they can directly effect on system's performance. It is evident that because of hidden attributes in the measurements such as correlation and nonlinearity, thresholding systems faced wrong separation defining by Missed Alarm Rate (MAR) and False Alarm Rate (FAR). This study introduced a novel extended adaptive thresholding based on mean-change point detection algorithm and shows that it is more efficient than other existing thresholding algorithm in the literature. Number hypothetical and industrial examples are given to delineate the capabilities and limitation of proposed method and prove its effectiveness in an industrial alarm system.

Presentation : "A Novel Extended Adaptive Thresholding for Industrial Alarm Systems"

This study proposes a novel chaotic anti-control for flexible joint system. The proposed controller is composed of a Lyapunov rule-based fuzzy control and chaotic anti-control for target tracking of the flexible joint manipulator. Chaotic signal is used to study the effect of anti-control to reduce the deflection of flexible joint system and control signal energy. For this purposes the flexible joint has been synchronized with chaotic Lorenz system. In this study on of the Lorenz parameters is changed to analysis the effect of chaotic signals. The results of the proposed approach shows in terms of level of vibration reduction and energy consumption of control signal, we could find an optimum point based on value of Lorenz system parameter. Finally, the efficacy of the proposed method and results of existence of different nonlinearity behavior is validated through experiments on QUANSER's flexible-joint manipulator.

Lyapunov rule-based fuzzy control and chaotic anti-control for flexible joint system and analysis of chaotic signal existence effectiveness with experimental validation

This paper presents a Gaussian radial basis function neural network based on sliding mode control for trajectory tracking and vibration control of a flexible joint manipulator. To study the effectiveness of the controllers, designed controller is developed for tip angular position control of a flexible joint manipulator. The adaptation laws of designed controller are obtained based on sliding mode control methodology without calculating the Jacobian of the flexible joint system. Also in this study, the anti-control is applied to reduce the deflection angle of flexible joint system. To achieve this goal, the chaos dynamic must be created in the flexible joint system. So, the flexible joint system has been synchronized to chaotic gyroscope system. In this study, control and anti-control concepts are applied to achieve the high quality performance of flexible joint system. It is tried to design a controller which is capable to satisfy the control and anti- control aims. The performances of the proposed control are examined in terms of input tracking capability, level of vibration reduction and time response specifications. Finally, the efficacy of the proposed method is validated through experimentation on QUANSERâs flexible-joint manipulator.

http://ijr.kntu.ac.ir/article_12510_b576b0b337a61343dc54de1c335c05d3.pdf

Mahdi Aliyari Shoorehdeli

Papers

V94.2 industrial gas turbine compressor fouling detection based on system identification methods, neural networks and experimental data

Inverted Pendulum Fault Tolerant Control Based on Fuzzy Backstepping Design and Anti-Control of Chaos

Presentation : "A Novel Extended Adaptive Thresholding for Industrial Alarm Systems"

Lyapunov rule-based fuzzy control and chaotic anti-control for flexible joint system and analysis of chaotic signal existence effectiveness with experimental validation

Hybrid Concepts of the Control and Anti-Control of Flexible Joint Manipulator