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 study, a hybrid learning algorithm for training the recurrent fuzzy neural network (RFNN) is introduced. This learning algorithm aims to solve main problems of the gradient descent (GD) based methods for the optimization of the RFNNs, which are instability, local minima and the problem of generalization of trained network to the test data. PSO as a global optimizer is used to optimize the parameters of the membership functions and the GD algorithm is used to optimize the consequent part's parameters of RFNN. As PSO is a derivative free optimization technique, a simpler method for the train of RFNN is achieved. Also the results are compared to GD algorithm.

Hybrid Training of Recurrent Fuzzy Neural Network Model

Domain Adversarial Neural Network Regression to design transferable soft sensor in a power plant

This paper describes hybrid multivariate method: Principal Component Analysis improved by Genetic Algorithm. This method determines main Principal Components can be used to detect fault during the operation of industrial process by neural classifier. This technique is applied to simulated data collected from the Tennessee Eastman chemical plant simulator which was designed to simulate a wide variety of faults occurring in a chemical plant based on a facility at Eastman chemical.

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Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier

This paper proposes a hybrid control scheme for the synchronization of two chaotic nonlinear gyros, subject to uncertainties and external disturbances. In this scheme, Linear Quadratic Regulation (LQR) control, Sliding Mode (SM) control and Gaussian Radial basis Function Neural Network (GRBFNN) control are combined. By Lyapunov stability theory, SM control is presented to ensure the stability of the controlled system. GRBFNN control is trained during the control process. The learning algorithm of the GRBFNN is based on the minimization of a cost function which considers the sliding surface and control effort. Simulation results demonstrate the ability of the hybrid control scheme to synchronize the chaotic gyro systems through the application of a single control signal.

Chaos synchronization of uncertain nonlinear gyros via hybrid control

Overhead crane is an industrial structure that used widely in many harbors and factories. It is usually operated manually or by some conventional control methods. In this paper, we propose a hybrid controller includes both position regulation and anti-swing control. According to Takagi-Sugeno fuzzy model of an overhead crane and genetic algorithm, a fuzzy controller is designed with parallel distributed compensation and Linear Quadratic Regulation. Using genetic algorithm, important fuzzy rules are selected and so the number of rules decreased and design procedure need less computation and its computation needs less time. Further, the stability of the overhead crane with the parallel distributed fuzzy LQR controller is discussed. The stability analysis and control design problems is reduced to linear matrix inequality (LMI) problems. Simulation results illustrated the validity of the proposed parallel distributed fuzzy LQR control method and it was compared with a similar method parallel distributed fuzzy controller with same fuzzy rule set.

Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection

Mahdi Aliyari Shoorehdeli

Papers

Hybrid Training of Recurrent Fuzzy Neural Network Model

Domain Adversarial Neural Network Regression to design transferable soft sensor in a power plant

Fault Detection of the Tennessee Eastman Process Using Improved PCA and Neural Classifier

Chaos synchronization of uncertain nonlinear gyros via hybrid control

Anti-swing control for a double-pendulum-type overhead crane via parallel distributed fuzzy LQR controller combined with genetic fuzzy rule set selection