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.

/pdf/a-survey-on-evolutionary-computation-approaches-to-feature-5c2qznm5vr.pdf

A Survey on Evolutionary Computation Approaches to Feature Selection

Binary grey wolf optimization approaches for feature selection

Pattern recognition

Intelligent Control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot via Gaussian RBF neural network based on sliding mode control

This study presents fault tolerant control of inverted pendulum via on-line fuzzy backstepping and anti-control of chaos The inverted pendulum is used frequently in robotic applications and can be found in different forms Based on Lyapunov stability theory for backstepping design, the nonlinear controller and some generic sufficient conditions for asymptotic control are attained Also in this study, anti-control of chaos is applied to increase the fault tolerant of inverted pendulum To achieve this goal, the chaos dynamic must be created in the inverted pendulum system So, the inverted pendulum 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 inverted pendulum system The performances of the proposed control are examined in terms of fault tolerant capability Finally, the efficacies of the proposed methods are illustrated by simulations

	 

	Key words: Backstepping design, anti-control, chaos, synchronization, chaotic gyroscope, on-line fuzzy design, inverted pendulum, fault tolerant control

https://www.academicjournals.org/article/article1380539961_Saedian et al.pdf

Fault tolerant control of mechatronics system based on hybrid control

Robust Anomaly Detection Based on a Dynamical Observer for Continuous Linear Roesser Systems

/pdf/to-analysis-and-compare-21-weight-constraints-in-stochastic-2ay1ssbl.pdf

To Analysis and Compare 21 Weight Constraints in Stochastic Gradient Descent Algorithm Using Kernel Method

In this study, first by using the collected real data from a 10000 cubic - meter Qazvin - kowsar water supply reservoir is modeled by nonlinear output error (NOE) structure, then a neural nonlinear controller based on the MLP neural network according to created model is designed in order to control the tank water level. The operation of the proposed controller is compared by a PID controller which its coefficients is optimized by genetic algorithm. Results of the simulation indicates that the neural nonlinear controller has a better function than the PID controller, and also this controller is able to control the level water of the tank appropriately regardless the consumer profile in all conditions even in consumer picks.

Mahdi Aliyari Shoorehdeli

Papers

Intelligent Control of human prosthetic eye movements system for the emotional support by a huggable pet-type robot via Gaussian RBF neural network based on sliding mode control

Fault tolerant control of mechatronics system based on hybrid control

Robust Anomaly Detection Based on a Dynamical Observer for Continuous Linear Roesser Systems

To Analysis and Compare 21 Weight Constraints in Stochastic Gradient Descent Algorithm Using Kernel Method

Identification and control of water supply reservoirs by using neural networks