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A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

ENERGIES 2022年第5期15卷 1705-1705页

作者： Firouzi, Behnam Alattas, Khalid A. Bakouri, Mohsen Alanazi, Abdullah K. Mohammadzadeh, Ardashir Mobayen, Saleh Fekih, Afef Ozyegin Univ Mech Engn Dept Vibrat & Acoust Lab VAL TR-34794 Istanbul Turkey Univ Jeddah Coll Comp Sci & Engn Dept Comp Sci & Artificial Intelligence Jeddah 21959 Saudi Arabia Majmaah Univ Coll Appl Med Sci Dept Med Equipment Technol Majmaah 11952 Saudi Arabia Fezzan Univ Dept Phys Coll Arts Traghen 71340 Libya Taif Univ Fac Sci Dept Chem POB 11099 At Taif 21944 Saudi Arabia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Sch Engn & Technol Da Nang 550000 Vietnam Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA

This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system's stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

关键词： wind turbine intelligent control type-2 fuzzy control (T2FLC) learning algorithm tension leg platforms (TLP)

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Representational Capabilities and learning of Bithreshold Neural Networks

Representational Capabilities and Learning of Bithreshold Ne...

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International Scientific Conference on Intellectual Systems of Decision-Making and Problems of Computational Intelligence (ISDMCI)

作者： Kotsovsky, Vladyslav Batyuk, Anatoliy Uzhgorod Natl Univ Uzhgorod Ukraine Lviv Polytech Natl Univ Lvov Ukraine

ISBN: (纸本)9783030542153;9783030542146

The paper deals with questions related to the ability of real-weighted bithreshold neurons and neural networks to solve the classification tasks. We study how many partitions of a finite set in n-dimensional vector space can be computed by using bithreshold neurons. The upper and lower bounds on the number of bithreshold dichotomies are proved. Our main theoretical result states that there exists Theta(m(n+1)) bithreshold dichotomies of the set of m points in general position in n-dimensional space. We also estimate the Vapnik-Chervonenkis dimension of the class of bithreshold neurons. The problem of the synthesis of 2-layer bithreshold neural network for binary classification is considered. We demonstrate that an arbitrary dichotomy of m points in general position in n-dimensional space can be realized by 2-layer neural network containing at most inverted right perpendicularm/(2n)inverted left perpendicular bithreshold neurons in hidden layer and a single output threshold neuron. The learning algorithm with computational complexity O(mn(2) + m(2)) is proposed for such networks. We give simulation results of the network performance on real data sets and discuss the influence of algorithm parameters on the quality of learning and the representational capabilities of bithreshold networks.

关键词： Bithreshold neuron Neural network Bithreshold network Classification learning algorithm

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learning BASED RECONFIGURABLE SUB-NYQUIST SAMPLING FRAMEWORK FOR ULTRA-WIDEBAND ANGULAR SENSING

LEARNING BASED RECONFIGURABLE SUB-NYQUIST SAMPLING FRAMEWORK...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Joshi, Himani Alaee-Kerahroodi, Mohammad Kumar, A. Anil Mysore, Bhavani Shankar R. Darak, Sumit J. IIIT Delhi ECE Dept New Delhi India Univ Luxembourg SnT Luxembourg Luxembourg TCS Innovat Lab Bangalore Karnataka India

ISBN: (纸本)9781509066315

In this work, an intelligent and reconfigurable ultra-wideband angular sensing (UWAS) framework is proposed which is independent of the maximum number of active transmissions in a wideband spectrum unlike the existing UWAS methods. To perform the above task, we propose a sub-Nyquist sampling and sparse ruler based multi-antenna array receiver architecture. By characterizing and selecting a set of frequency bands via a learning algorithm, the proposed receiver allows sensing of more active transmissions than the number of antenna over an unlimited bandwidth. The simulation results show that due to the learning based approach, the proposed UWAS outperforms when compared to non-learning based UWAS method.

关键词： learning algorithm reconfigurable sub-Nyquist sampling wideband angular sensing

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A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation

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APPLIED SOFT COMPUTING 2020年 91卷 106241-106241页

作者： Mohammadzadeh, Ardashir Kumbasar, Tufan Univ Bonab Elect Engn Dept Bonab Iran Istanbul Tech Univ Dept Control & Automat Engn Istanbul Turkey

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman's model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared with the other T2 fuzzy and well-known model predictive controllers. The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system. (C) 2020 Elsevier B.V. All rights reserved.

关键词： General Type-2 Fuzzy Logic Controller Type-1 diabetes learning algorithm Fractional-order Stability analysis

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A novel optimal PID controller autotuning design based on the SLP algorithm

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EXPERT SYSTEMS 2020年第2期37卷 e12489-e12489页

作者： Pongfai, Jirapun Su, Xiaojie Zhang, Huiyan Assawinchaichote, Wudhichai King Mongkuts Univ Technol Thonburi Fac Engn Dept Elect & Telecommun Engn Bangkok Thailand Chongqing Univ Coll Automat Chongqing Peoples R China Chongqing Technol & Business Univ Natl Res Base Intelligent Mfg Serv Chongqing Peoples R China

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the "swarm learning process" (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder-Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

关键词： artificial intelligence control system learning algorithm optimization particle swarm PID controller

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Recognizing ragas of Carnatic genre using advanced intelligence: a classification system for Indian music

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第3期54卷 383-405页

作者： Kumaraswamy, Balachandra Poonacha, P. G. Int Inst Informat Technol Bangalore Karnataka India BMS Coll Engn Bangalore Karnataka India

Purpose In general, Indian Classical Music (ICM) is classified into two: Carnatic and Hindustani. Even though, both the music formats have a similar foundation, the way of presentation is varied in many manners. The fundamental components of ICM are raga and taala. Taala basically represents the rhythmic patterns or beats (Dandawate et al., 2015;Kirthika and Chattamvelli, 2012). Raga is determined from the flow of swaras (notes), which is denoted as the wider terminology. The raga is defined based on some vital factors such as swaras, aarohana-avarohna and typical phrases. Technically, the fundamental frequency is swara, which is definite through duration. Moreover, there are many other problems for automatic raga recognition model. Thus, in this work, raga is recognized without utilizing explicit note series information and necessary to adopt an efficient classification model. Design/methodology/approach This paper proposes an efficient raga identification system through which music of Carnatic genre can be effectively recognized. This paper also proposes an adaptive classifier based on NN in which the feature set is used for learning. The adaptive classifier exploits advanced metaheuristic-based learning algorithm to get the knowledge of the extracted feature set. Since the learning algorithm plays a crucial role in defining the precision of the raga recognition, this model prefers to use the GWO. Findings Through the performance analysis, it is witnessed that the accuracy of proposed model is 16.6% better than NN with LM, NN with GD and NN with FF respectively, 14.7% better than NN with PSO. Specificity measure of the proposed model is 19.6, 24.0, 13.5 and 17.5% superior to NN with LM, NN with GD, NN with FF and NN with PSO, respectively. NPV of the proposed model is 19.6, 24, 13.5 and 17.5% better than NN with LM, NN with GD, NN with FF and NN with PSO, respectively. Thus it has proven that the proposed model has provided the best result than other conventional

关键词： Raga identification Swara identification NN GWO learning algorithm

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Enabling Intelligence at Network Edge: An Overview of Federated learning

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ZTE Communications 2020年第2期18卷 2-10页

作者： Howard H.YANG ZHAO Zhongyuan Tony Q.S.QUEK Singapore University of Technology and Design Singapore 487372Singapore Beijing University of Post and Telecommunication Beijing 100876China

The burgeoning advances in machine learning and wireless technologies are forg?ing a new paradigm for future networks, which are expected to possess higher degrees of in?telligence via the inference from vast dataset and being able to respond to local events in a timely manner. Due to the sheer volume of data generated by end-user devices, as well as the increasing concerns about sharing private information, a new branch of machine learn?ing models, namely federated learning, has emerged from the intersection of artificial intelli?gence and edge computing. In contrast to conventional machine learning methods, federated learning brings the models directly to the device for training, where only the resultant param?eters shall be sent to the edge servers. The local copies of the model on the devices bring along great advantages of eliminating network latency and preserving data privacy. Never?theless, to make federated learning possible, one needs to tackle new challenges that require a fundamental departure from standard methods designed for distributed optimizations. In this paper, we aim to deliver a comprehensive introduction of federated learning. Specifical?ly, we first survey the basis of federated learning, including its learning structure and the distinct features from conventional machine learning models. We then enumerate several critical issues associated with the deployment of federated learning in a wireless network, and show why and how technologies should be jointly integrated to facilitate the full imple?mentation from different perspectives, ranging from algorithmic design, on-device training, to communication resource management. Finally, we conclude by shedding light on some po?tential applications and future trends.

关键词： federated learning edge intelligence learning algorithm communication efficiency privacy and security

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Automatic Exploration Process Adjustment for Safe Reinforcement learning with Joint Chance Constraint Satisfaction 21st

Automatic Exploration Process Adjustment for Safe Reinforcem...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Okawa, Yoshihiro Sasaki, Tomotake Iwane, Hidenao Fujitsu Labs Ltd Artificial Intelligence Lab Nakahara Ku 4-1-1 Kamikodanaka Kawasaki Kanagawa 2118588 Japan Reading Skill Test Inc Res & IT Div Chuo Ku 2-1-4 Shinkawa Tokyo 1040033 Japan Fujitsu Labs Ltd Kawasaki Kanagawa Japan

In reinforcement learning (RL) algorithms, exploratory control inputs are used during learning to acquire knowledge for decision making and control, while the true dynamics of a controlled object is unknown. However, this exploring property sometimes causes undesired situations by violating constraints regarding the state of the controlled object. In this paper, we propose an automatic exploration process adjustment method for safe RL in continuous state and action spaces utilizing a linear nominal model of the controlled object. Specifically, our proposed method automatically selects whether the exploratory input is used or not at each time depending on the state and its predicted value as well as adjusts the variance-covariance matrix used in the Gaussian policy for exploration. We also show that our exploration process adjustment method theoretically guarantees the satisfaction of the constraints with the pre-specified probability, that is, the satisfaction of a joint chance constraint at every time. Finally, we illustrate the validity and the effectiveness of our method through numerical simulation. Copyright (C) 2020 The Authors.

关键词： Reinforcement learning learning algorithm Safe exploration Safety-critical Chance constraint

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Training feedforward neural network via multiobjective optimization model using non-smooth L1/2 regularization

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NEUROCOMPUTING 2020年 410卷 1-11页

作者： Senhaji, Kaoutar Ramchoun, Hassan Ettaouil, Mohamed Sidi Mohamed Ben Abdellah Univ Lab Modeling & Sci Comp FST Fez Fes Morocco Moulay Ismail Univ Natl Sch Business & Management Lab Math Modeling Simulat & Smart Syst Meknes Morocco BCAM Basque Ctr Appl Math Bilbao Spain

The paper presents a new approach to optimize the Multilayer Perceptron Neural Network (MLPNN), to deal with the generalization problem. As known, most supervised learning algorithms aim to minimize the training error. However, the mentioned methods, based only on error minimizing, may generate a solution with an insufficient generalization performance. This present work proposes a multiobjective modelling problem involving two objectives: accuracy and complexity since the learning problem is multiobjective by nature. The learning task is carried on by minimizing both objectives simultaneously, according to Pareto domination concept, using NSGAII (Non-dominated Sorting Genetic algorithm II) as a solver. This method leads us to a set of solutions called Pareto front, being the optimal solutions set, the adequate MLPNN need to be extracted. We show empirically that the proposed method is capable of reducing the neural networks topology and improved generalization performance, in addition to a good classification rate compared to different methods. (C) 2020 Published by Elsevier B.V.

关键词： Multiobjective optimization NSGAII learning algorithm L-1/2 regularization Neural network

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An intelligent method for online voltage stability margin assessment using optimized ANFIS and associated rules technique

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ISA TRANSACTIONS 2020年 102卷 91-104页

作者： Ghaghishpour, Amin Koochaki, Amangaldi Islamic Azad Univ Aliabad Katoul Branch Dept Elect Engn Aliabad Katoul Iran

This paper puts forward an intelligent method for online voltage stability margin (VSM) assessment based on optimal fuzzy system and feature selection technique, which has excellent performance for large power systems. The proposed VSM estimation method includes three key parts: feature extraction and selection part, estimator part and training part. In this method, power system's loading parameters are used as the main input of adaptive neuro-fuzzy inference system (ANFIS) and association rules (AR) technique is used to select the most effective loading parameters. In the training part, we used Harris hawks optimization algorithm (HHOA) to train the ANFIS efficiently. Using the proposed method, the VSM can be monitored online with high precision for both small and large systems and appropriate control measures can be applied if necessary. Knowing the exact amount of VSM and applying precautionary measures can prevent from voltage collapse, heavy financial losses and power supply interruption. The proposed method tested on 39-bus, 118-bus and 300-bus IEEE test system and the MATLAB simulation results demonstrated that the propounded method has much better performance than other recently introduced VSM assessment approaches. Providing a VSM estimation method, which is effective for large power systems, selecting the most informative loading parameters and improving the ANFIS's performance using HHOA are the main contributions of this paper. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： ANFIS Estimation Feature selection learning algorithm Voltage stability

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