Mahdi Aliyari Shoorehdeli

Bio: Mahdi Aliyari Shoorehdeli is an academic researcher from K.N.Toosi University of Technology. The author has contributed to research in topics: Fuzzy control system & Control theory. The author has an hindex of 20, co-authored 157 publications receiving 1812 citations. Previous affiliations of Mahdi Aliyari Shoorehdeli include Islamic Azad University, Science and Research Branch, Tehran & Islamic Azad University.

Multidisciplinary modeling and position control of an electro-hydrostatic actuation system using an adaptive PID controller based on neurofuzzy network

Abstract: This paper deals with the issue of position control of an Electro-Hydrostatic Actuator (EHA) using an adaptive PID controller based on neurofuzzy network. In this relation, the design and simulation of an electro-hydrostatic actuation system referred to as EHA using multidisciplinary modeling method is presented. In recent years, fuzzy-PID controller is one of the main controllers that apply to the EHA systems. To improve the response of this controller, another control technique is needed to combine with the fuzzy-PID, and also, training some parameters of fuzzy-PID technique is a solution. The whole of new controller is composed of pair of interconnected subsystems, that is, an RBF network and conventional fuzzy-PID controller to enhance the tracking performance. Results show a significant improvement in transient response is achieved in comparison with a conventional fuzzy-PID control.

Velocity control of EHSS by using Mamdani and ANFIS controllers

Abstract: In this paper a Mamdani type fuzzy system and an adaptive network based fuzzy inference system (ANFIS) are presented for velocity control of an electro hydraulic servo system (EHSS) in presence of flow nonlinearities and internal friction. The architecture and learning procedure ANFIS is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. It is shown that both these controllers can be successfully used to stabilize any chosen operating point of the system. All derived results are validated by computer simulation of nonlinear mathematical model of the system.

Evolutionary Algorithms to Compute the Optimal Parameters of Gaussian Radial Basis Adaptive Backstepping Control for Chaotic Systems

Abstract: In this paper, evolutionary algorithms are proposed to compute the optimal parameters of Gaussian Radial Basis Adaptive Backstepping Control (GRBABC) for chaotic systems. Generally, parameters are chosen arbitrarily, so in several cases this choice can be tedious. Also, stability cannot be achieved when the parameters are inappropriately chosen. The optimal design problems are to introduce optimization algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO) in order to find the optimal parameters which minimize a cost function defined as an error quadratic function. These methods are applied to two chaotic systems; Duffing Oscillator and Lu systems. Simulation results verify that our proposed algorithms can achieve enhanced tracking performance regarding similar methods.

Generalized projective synchronization of time-delayed chaotic systems via sliding adaptive radial basis function neural network control

Abstract: Summary from only given. In this study, generalized projective synchronization (GPS) of two identical and nonidentical time-delayed chaotic systems is presented. Sliding adaptive radial basis function neural network control (SARBFNNC) is applied to synchronize two delayed chaotic systems. The advantages of the adaptive control, neural network and sliding mode control theory are combined in the proposed method. The stability of error dynamics is guaranteed with Lyapunov stability theory. Moreover, supposing that the parameters of the chaotic system are unknown, recursive least square (RLS) method is applied to estimate these unknown parameters. The proposed method has not been used for synchronization of time-delayed chaotic systems yet. Simulation results show that the proposed method is suitable and effective for synchronization of time-delayed chaotic systems.

Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization

Abstract: —There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data-based fault detection methods proposed by arti  cial intelligence applications. In this paper, we propose a framework for fault detection based on reinforcement learning and a policy known as proximal policy optimization. As a result of the lack of fault data, one of the signi  cant problems with the traditional policy is its weakness in detecting fault classes, which was addressed by changing the cost function. Using modi  ed Proximal Policy Optimization, we can increase performance, over-come data imbalance, and better predict future faults. When our modi  ed policy is implemented, all evaluation metrics will increase by 3% to 4% as compared to the traditional policy in the  rst benchmark, between 20% and 55% in the second benchmark, and between 6% and 14% in the third benchmark, as well as an improvement in performance and prediction speed compared to previous methods.

Adaptive Control

Abstract: 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.

Learning from class-imbalanced data

Abstract: 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.

A Survey on Evolutionary Computation Approaches to Feature Selection

Abstract: 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.