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32nd International Conference on Electrical Engineering (ICEE)

作者： Attar, Arvin Badamchizadeh, Mohammad Ali Ghaemi, Sehraneh Univ Tabriz Fac Elect & Comp Engn Tabriz Iran

ISBN: (纸本)9798350376357;9798350376340

Nowadays, fuzzy sets have attracted a lot of attention in control systems. Type 2 fuzzy controllers (T2FCs) have replaced type 1 fuzzy controllers to solve the challenge of uncertainty in nonlinear systems. According to the various applications of multiagent systems (MASs) in control science, in this research, an adaptive type 2 fuzzy controller (AT2FC) with the gradient descent method has been designed for a class of nonlinear multi-agent system to arrange this nonlinear system in a leader-follower formation with the slightest error. Mamdani rules and different membership functions are used in this controller. Also, based on control science's use of meta-heuristic algorithms, the jaya algorithm has been applied to this system to adjust and optimize the parameters of the adaptive rules of this controller. The simulation results have been analyzed on trajectory tracking performance and position control of three quadrotors, including one leader and two followers.

关键词： nonlinear systems type 2 fuzzy controller multiagent systems leader-follower formation jaya optimization algorithm

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Improving Neural Network Using jaya algorithm with Opposite Learning for Air Quality Prediction

Improving Neural Network Using Jaya Algorithm with Opposite ...

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Intelligent Systems Conference (IntelliSys)

作者： Abu Doush, Iyad Sultan, Khalid Alsaber, Ahmad Alkandari, Dhari Abdullah, Afsah Amer Univ Kuwait Dept Comp Coll Engn & Appl Sci Salmiya Kuwait Amer Univ Kuwait Dept Comp Engn Coll Engn & Appl Sci Salmiya Kuwait Amer Univ Kuwait Dept Management Coll Business & Econ 15 Salem Mubarak StSalmiya POB 3323 Safat 13034 Kuwait Kuwait Univ Dept Earth & Environm Sci POB 25944 Safat 1320 Kuwait Amer Univ Kuwait Off Res & Grants Salmiya Kuwait

ISBN: (纸本)9783031477201;9783031477218

The Multi-Layer Perceptron Neural Network (MLP) is the commonly used Feedforward Neural Network (FNN) for tackling classification and prediction problems. The efficiency of MLP relies on the appropriate selection of its weights and biases. Usually, a gradient-based technique is used for tuning the selection of these parameters during the learning process. This technique suffers from its slow convergence and being stuck in local optima. Predicting urban air quality is vital to prevent urban air pollution and improve the life of residents. The air quality index (AQI) is a quantitative air quality tool. In this paper, an enhanced jaya optimization algorithm is used to improve the MLP outcome (called EOL-jaya-MLP). The opposite-learning method is used to improve the algorithm search space exploration. A three-year dataset from air quality monitoring stations is used in this study. The proposed technique is compared against the original jaya and six machine learning techniques. Interestingly, the EOL-jaya-MLP outperforms other techniques when predicting the AQI.

关键词： jaya optimization algorithm optimization Feedforward Neural Networks Multi-Layer Perceptron Swarm Intelligence

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Correction to: selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Basil, Noorulden Marhoon, Hamzah M. Department of Electrical Engineering College of Engineering Mustansiriyah University Baghdad Iraq Department of Systems Engineering College of Information Engineering Al-Nahrain University Baghdad Jadriya Iraq

Recently, there has been a growing interest among academics worldwide in studying flight control systems. The advancement of tracking technologies, such as the X-15 adaptive flight control system developed at NASA (National Aeronautics and Space Administration), has sparked significant exploration efforts by scientists. The vast availability of aerial resources further contributes to the importance of studying adaptive flight control systems (AFCS). The successful operation of AFCS relies on effectively managing the three fundamental motions: pitch, roll, and yaw. Therefore, scientists have been diligently working on developing optimization algorithms and models to assist AFCS in achieving optimal gains during motion. However, in real-world scenarios, each motion requires its own set of criteria, which presents several challenges. Firstly, there are multiple criteria available for selecting appropriate optimization values for each motion. Secondly, the relative importance of these criteria influences the selection process. Thirdly, there is a trade-off between the performance of the criteria within a single optimization case and across different cases. Lastly, determining the critical value of the criteria poses another obstacle. Consequently, evaluating and selecting optimum methods for AFCS trajectory controls becomes a complex operation. To address the need for optimizing AFCS for various maneuvers, this study proposes a new selection process. The suggested approach involves utilizing black hole optimization (BHO), jaya optimization algorithm (JOA), and sunflower optimization (SFO) as methods for detecting and correcting trajectories in adaptive flight control systems. These methods aim to determine the best launch of missiles from the AFCS based on the coordinate location for both long and short distances. Additionally, the methods determine the optimal gains for the FOPID (fractional order proportional integral derivative) controller and enhance protection agai

关键词： Adaptive flight control system black hole optimization fopid controller criteria jaya optimization algorithm Sunflower optimization

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Selection and evaluation of FOPID criteria for the X-15 adaptive flight control system (AFCS) via Lyapunov candidates: Optimizing trade-offs and critical values using optimization algorithms

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2023年 6卷

关键词： Adaptive flight control system Black hole optimization FOPID controller criteria jaya optimization algorithm Sunflower optimization

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Real-time measurement method of drilling fluid rheological parameters based on multi-objective inversion

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MEASUREMENT 2023年 223卷

作者： Zou, Jialing Ni, Yuanlei Liang, Haibo Southwest Petr Univ Sch Mech Engn Chengdu 610500 Peoples R China Southwest Petr Univ Mech Engn 8 Xindu Rd Chengdu Sichuan Peoples R China

Real-time online fast and accurate measurement of drilling fluid performance parameters is very important in the petroleum extraction process. At present, there are few studies on the measurement of drilling fluid performance parameters, and the measurement of drilling fluid performance parameters on the well site mainly uses a rotational viscometer, which cannot achieve online real-time measurement. Therefore, this study developed a real-time online measurement system for drilling fluid rheological performance parameters based on the principle of pipeline differential pressure measurement, and realized online measurement of parameters such as yield stress, liquidity index, and consistency coefficient. In order to obtain accurate three rheological parameters, this study conducted a numerical simulation study of the drilling fluid measurement system based on the finite element method to realize the analysis of fluid pressure distribution in the pipeline. Secondly, the surrogate model between the differential pressure and rheological parameters, which is based on an extreme learning machine (ELM) optimized by particle swarm algorithm (PSO) is set up. Finally, based on the accuracy and robustness of the multi-objective function of inversion calculation, an intelligent inversion model for rheological parameters with a multi-objective jaya algorithm is proposed to realize real-time online fast and accurate acquisition of drilling fluid rheological parameters.

关键词： Drilling fluids Rheological properties parameters Multi -target inversion Extreme Learning Machine jaya optimization algorithm

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A Consideration of the Single-Phase Photovoltaic and Energy Storage Joint Regulation of a Three-Phase Unbalanced Control Strategy in a Power Distribution System

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ENERGIES 2023年第12期16卷 4817页

作者： Wang, Min Wang, Huilin Zuo, Fanglin Zou, Jie Chen, Yuan Yu, Zixuan Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Peoples R China

With a large amount of distributed power and energy storage access, the traditional three-phase unbalanced treatment of a power distribution system is mainly aimed at the three-phase unbalance of a load, which cannot effectively address the three-phase unbalance problem of a power distribution network after a large number of single-phase photovoltaic access. Therefore, this paper proposes a three-phase unbalanced treatment strategy for the distribution network, which considers the joint regulation ability of single-phase photovoltaic and energy storage and the regulation ability of a reactive power compensation device. Firstly, the joint regulation ability of single-phase photovoltaic and energy storage under different photovoltaic permeability is analyzed. Secondly, according to the joint regulation ability of single-phase photovoltaic and energy storage and the regulation ability of reactive power compensation device, the three-phase power optimization model is constructed to minimize the three-phase unbalance degree and regulation cost, and the jaya optimization algorithm is used to solve the model. Finally, the 33-node distribution system is used to verify the effectiveness of the proposed strategy.

关键词： single-phase photovoltaic and energy storage power distribution system three-phase unbalance jaya optimization algorithm

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Multi-parameter inverse analysis of concrete dams using kernel extreme learning machines-based response surface model

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ENGINEERING STRUCTURES 2022年 256卷 113999-113999页

作者： Kang, Fei Liu, Xi Li, Junjie Li, Hongjun Dalian Univ Technol Fac Infrastruct Engn Sch Hydraul Engn Dalian 116024 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100048 Peoples R China

Inverse analysis by finite element model (FEM) based on measured displacement data is a popular approach for parameter identification of concrete dams. FEM-based inverse analysis method is economical and effective, but with some limitations such as complex computation and time-consuming. This paper presents a kernel extreme learning machine (KELM)-based response surface model (RSM) for parameter inverse analysis of concrete dams. The FEM is replaced by KELM-based RSM to explore the relationships between material parameters and displacement response of dam-foundation systems. Sample data of KELM-based RSM are generated by the efficient sampling technique Latin hypercube sampling. Subsequently, a novel optimization algorithm jaya is adopted to minimize the objective function for material parameters identification. The effectiveness and practicability of the proposed approach are verified by two cases and two real concrete gravity dams in operation with sufficient monitoring data. Comparative studies with direct finite element method and several optimization algorithms were also performed to demonstrate the superiority of the proposed approach over commonly used techniques. Results show that the proposed KELM-based RSM is a simple and efficient way to achieve high accuracy in parameter inverse analysis of concrete dams at a low computation cost.

关键词： Concrete dams Inverse back analysis Kernel extreme learning machines jaya optimization algorithm Displacements

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A Hybrid DL Architecture for Improved Generalizability with Self-Adaptive jaya Optimizer for Diabetic Retinopathy

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Procedia Computer Science 2024年 235卷 2090-2100页

作者： Akhilesh Rawat Rajeev Kumar Data to Knowledge (D2K) Lab School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi 110 067 India

Anomalies such as microaneurysms, exudates, and hemorrhages are diagnosed by retinal vessel segmentation to identify different phases of Diabetic Retinopathy (DR); several typical ways to detect Hard Exudates (HE) in retinal images have been used to determine the severity of diabetes. This work proposes a hybrid approach to better segment such abnormal cells. It optimizes the learning parameters of a Convolutional Neural Network (CNN) with a nature-inspired optimizer, the Self-Adaptive jaya optimization algorithm (SAJOA). The SAJOA helps in avoiding trapping in local minima. It uses the SAJOA to steer the search for obtaining near-optimal parameter values for the CNN training. We choose the learning parameters such as batch size, dropout rate, learning rate, etc. The batch size affects the batch normalization performance, and the dropout rate involves the regularization property of the model. The near-optimal values of these two parameters help improve the generalization ability of the deep learning architecture. Thus, this hybrid approach improves the CNN performance and generalizability, leading to precise segmentation of unseen data, which aids in diagnosing diabetic retinopathy. The results of many trials were evaluated against the DRIVE dataset; the proposed method achieved accuracy and F1 score on average, higher by 7 to 8% than the state-of-the-art methods.

关键词： Diabetic Retinopathy Hard Exudates Deep Learning Convolutional Neural Network Nature-Inspired algorithms jaya optimization algorithm Generalization

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Performance Evaluation of jaya algorithm During Search Space Violation 22

Performance Evaluation of Jaya Algorithm During Search Space...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： P. D. Dewangan Praveen Mande Ankur Gupta P. J. Krishna V. P. Meena V. P. Singh Department of Electrical Engineering NIT Raipur India Department of EECl Engineering Gandhi Institute of Tech. and Manag India School of CSE and Tech. Bennett University India Department of Electrical Engineering Malaviya National Inst. of Technology Jaipur India Department of Electrical Engineering Malaviya National Institute of Technology India Department of Electrical Engineering MNIT Jaipur India

ISBN: (纸本)9781450399937

In this work, the efficiency and performance of the jaya optimizer are evaluated for two different initialization methods. In the course of the entire iterative process, these starting approaches are used whenever an aspect of any explanation violates the exploration area. The violating aspect is initially set up in the first technique close to the edge of the exploration area. In the second, the violating aspect of the exploration area is initialized at irregularly. For performance comparison, eight different unimodal measure exercises are reduced using the modified jaya approach. The appraisal of the evenhanded exercise, or figure of merit, along with its mean, minimum, standard deviation, and maximum appraisals are used to compare efficacy and performance.

关键词： Swarm Intelligence Initialization Meta-heuristics Unimodal measure exercises jaya optimization algorithm

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Enhancement of Artificial Emotional Neural Network Using jaya algorithm and the Investigation of Expanded Feature Selected for Wind Power Forecasting

Enhancement of Artificial Emotional Neural Network Using JAY...

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2019第七届计算机与通信管理国际会议

作者： Suthasinee Iamsa-at Punyaphol Horata Khamron Sunat Department of Computer Science Faculty of Science Khon Kaen University

The Brain Emotional Learning(BEL) is a novel bio-inspired machine learning approach mentioned as a new class of artificial neural network(ANN).The artificial emotional neural network(AENN) is one of the BEL methods which used the genetic algorithm(GA) to compute proper weights,weights of the amygdala(AMYG),orbitofrontal cortex(OFC) weights and a bias *** trained by GA has been reported that it could produce low error ***,AENN still has more rooms to enhance its prediction of performance,especially generalization and the prediction of ***,this paper aims to propose a new training method for *** jaya optimization algorithm optimized the weights and the biases of *** new proposed models are named as AENN-Maxjaya and *** names are according to the way of selecting the additional expanded feature which obtained through either the max or the average of input patterns,*** the experimental results for wind power forecasting dataset,the proposed methods proved that the results are better in generalization performance and give lower error rates which compared to the comparative AENN models and traditional ANNs.

关键词： Brain Emotional Learning artificial emotional neural network jaya optimization algorithm forecasting wind power

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