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

The walking beam furnace (WBF) is one of the most prominent process plants
 often met in an alloy steel production factory and characterized by high
 non-linearity, strong coupling, time delay, large time-constant and time
 variation in its parameter set and structure. From another viewpoint, the
 WBF is a distributed-parameter process in which the distribution of
 temperature is not uniform. Hence, this process plant has complicated
 non-linear dynamic equations that have not worked out yet. In this paper, we
 propose one-step non-linear predictive model for a real WBF using non-linear
 black-box sub-system identification based on locally linear neuro-fuzzy
 (LLNF) model. Furthermore, a multi-step predictive model with a precise long
 prediction horizon (i.e., ninety seconds ahead), developed with application
 of the sequential one-step predictive models, is also presented for the
 first time. The locally linear model tree (LOLIMOT) which is a progressive
 tree-based algorithm trains these models. Comparing the performance of the
 one-step LLNF predictive models with their associated models obtained
 through least squares error (LSE) solution proves that all operating zones
 of the WBF are of non-linear sub-systems. The recorded data from Iran Alloy
 Steel factory is utilized for identification and evaluation of the proposed
 neuro-fuzzy predictive models of the WBF process.

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Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques

Recently a lot of works have been done to detect faults in nonlinear systems. In this paper a new method, based on parity relations for linear systems, is proposed to detect faults in nonlinear systems that can be modeled by Takagi-Sugeno (T.S) fuzzy system. This method is an intuitive generalization of parity relations, because T.S fuzzy system uses local linear models. Results of simulation and implementation on a rotary inverted pendulum show that faults can be detected very well.

Fault detection of nonlinear systems by parity relations

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.

A novel extended adaptive thresholding for industrial alarm systems

In this study, adaptive control of flexible link model which is non-minimum phase and single-input, multiple-output (SIMO) is presented. The controllers designed aim to control the hub position in a way that attenuates the tip deflections with less energy consumption. Methods used to design the under actuated controller are WRBF network and neuro-fuzzy network and are compared to LQR and non-adaptive fuzzy controller. Learning method performed for adaptive schemes is emotional. Simulation results show the effectiveness of the designed controllers and reduction of energy demand in intelligent adaptive controllers.

Adaptive intelligent control of flexible link robot arm

In This study, we present a new sensor fault detection approach based on nonlinear parity technique in presence of sensor noise. Conventionally analytical redundancy (AR) was used to fault detection and isolation in linear systems. The proposed parity space approach with nonlinear analytical redundancy (NLAR) technique can be applied to detect sensor fault in the nonlinear affine systems with mentioned class. The proposed approach will be implemented in pH neutralization system. At the end nonlinear fault detection and identification algorithm will be successfully implemented, examined and reported.

Mahdi Aliyari Shoorehdeli

Papers

Short-term and long-term thermal prediction of a walking beam furnace using neuro-fuzzy techniques

Fault detection of nonlinear systems by parity relations

A novel extended adaptive thresholding for industrial alarm systems

Adaptive intelligent control of flexible link robot arm

Nonlinear parity approach to sensor fault detection in pH neutralization system