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Dual periodic event-triggered control for multi-agent systems with input saturation

ISA TRANSACTIONS 2023年 136卷 61-74页

作者： Chen, Mengshen Yan, Huaicheng Zhang, Hao Fan, Sha Shen, Hao East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China Tongji Univ Dept Control Sci & Engn Shanghai 200092 Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243002 Peoples R China Chengdu Univ Sch Informat Sci & Engn Chengdu 610106 Peoples R China

This paper is concerned with the periodic event-triggered consensus of multi-agent systems subject to input saturation. Due to the nonlinearity caused by the input saturation constraint, the accuracy of the event-triggered mechanism to screen data will be reduced. To deal with this problem, a novel dual periodic event-triggered mechanism is first proposed, in which a saturation-assisted periodic event-trigger and a complemental periodic event-trigger work synergistically to screen data more efficiently under the input saturation constraint. In addition, considering the various disturbances in the environment, a more general mixed H infinity and passive performance is introduced to describe the disturbance attenuation level. Based on the Lyapunov-Krasovskii functional, some less conservative consensus criteria are obtained for the multi-agent systems. In addition, under different input satura-tion constraints, the relationship between the disturbance attenuation level and the data transmission rate is explored. After that, a particle swarm optimization algorithm is a first attempt to estimate and enlarge the region of asymptotic consensus. Finally, an example is given to verify the effectiveness and superiority of our proposed method. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Multi-agent systems Dual periodic event-triggered mechanism Input saturation particle swarm optimization algorithm

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Estimation of geomechanical rock characteristics from specific energy data using combination of wavelet transform with ANFIS-PSO algorithm

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2023年第8期13卷 1715-1740页

作者： Behboud, Mohammad Mohammadi Ramezanzadeh, Ahmad Tokhmechi, Behzad Mehrad, Mohammad Davoodi, Shadfar Shahrood Univ Technol Sch Min Petr & Geophys Engn Shahrood Iran Tomsk Polytech Univ Sch Earth Sci & Engn Lenin Ave Tomsk Russia

The geomechanical characteristics of a drill formation are uncontrollable factors that are crucial to determining the optimal controllable parameters for a drilling operation. In the present study, data collected in wells drilled in the Marun oilfield of southwestern Iran were used to develop adaptive network-based fuzzy inference system (ANFIS) models of geomechanical parameters. The drilling specific energy (DSE) of the formation was calculated using drilling parameters such as weight-on-bit (WOB), rate of penetration (ROP), rotational speed of drilling string (RPM), torque, bit section area, bit hydraulic factor, and bit hydraulic power. A stationary wavelet transform was subsequently used to decompose the DSE signal to the fourth level. The approximation values and details of each level served as inputs for ANFIS models using particle swarm optimization (PSO) algorithm and genetic algorithm (GA). As model outputs, the Young's Modulus, uniaxial compressive strength (UCS), cohesion coefficient, Poisson's ratio, and internal friction angle were compared to the geomechanical parameters obtained from petrophysical logs using laboratory-developed empirical relationships. Both models predicted the Young's modulus, UCS, and cohesion coefficient with high accuracy, but lacked accuracy in predicting the internal friction angle and Poisson's ratio. The root mean square error (RMSE) and determination coefficient (R-2) were lower for the ANFIS-PSO model than for the ANFIS-GA model, indicating that the ANFIS-PSO model presents higher accuracy and better generalization capability than the ANFIS-GA model. As drilling parameters are readily available, the proposed method can provide valuable information for strategizing a drilling operation in the absence of petrophysical logs.

关键词： Drilling specific energy ANFIS Wavelet function particle swarm optimization algorithm Genetic algorithm

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An improved Fourier Ptychography algorithm for ultrasonic array imaging

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期163卷 107157-107157页

作者： Yang, Yaoyao Wu, Runcong Chen, Dongdong Fei, Chunlong Li, Di Yang, Yintang Xidian Univ Sch Microelect Xian 710071 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Inspired by the optical imaging algorithm, the Fourier Ptychography (FP) algorithm is adopted to improve the resolution of ultrasonic array imaging. In the FP algorithm, the steady-state spectrum is utilized to recover the high-resolution ultrasonic images. Meanwhile, the parameters of FP algorithm are empirical, which can affect the imaging quality of ultrasonic array. Then the particle swarm optimization (PSO) algorithm is used to optimize the parameters of FP algorithm to further improve the imaging quality of ultrasonic array. The tungsten imaging experiments and pig eye imaging experiments are conducted to demonstrate the feasibility and effectiveness of the developed algorithm. In addition, the proposed algorithm and the coherent wave superposition (CWS) al-gorithm are both based on single plane wave (SPW) algorithms and they are then compared. The results show that the CWS algorithm and FP algorithm have good longitudinal and lateral resolutions, respectively. The particle swarm optimization-based FP (PSOFP) imaging algorithm has both excellent lateral and longitudinal resolutions. The average lateral resolution of PSOFP imaging algorithm is improved by 34.47% compared with CWS imaging algorithm in the tungsten wires experiments, and the lateral boundary structure width of the lens is improved by 49.48% in the pig eye experiments. The proposed algorithm can effectively improve the ultrasonic imaging quality for medical application.

关键词： Fourier Ptychography Ultrasonic imaging particle swarm optimization algorithm Medical imaging

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WPO: A Whale particle optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2023年第1期16卷 115-115页

作者： Huang, Ko-Wei Wu, Ze-Xue Jiang, Chang-Long Huang, Zih-Hao Lee, Shih-Hsiung Natl Kaohsiung Univ Sci & Technol Dept Elect Engn Kaohsiung Taiwan Natl Kaohsiung Univ Sci & Technol Dept Intelligent Commerce Kaohsiung Taiwan

Metaheuristic algorithms are novel optimization algorithms often inspired by nature. In recent years, scholars have proposed various metaheuristic algorithms, such as the genetic algorithm (GA), artificial bee colony, particle swarm optimization (PSO), crow search algorithm, and whale optimization algorithm (WOA), to solve optimization problems. Among these, PSO is the most commonly used. However, different algorithms have different limitations. For example, PSO is prone to premature convergence and falls into a local optimum, whereas GA coding is difficult and uncertain. Therefore, an algorithm that can increase the computing power and particle diversity can address the limitations of existing algorithms. Therefore, this paper proposes a hybrid algorithm, called whale particle optimization (WPO), that combines the advantages of the WOA and PSO to increase particle diversity and can jump out of the local optimum. The performance of the WPO algorithm was evaluated using four optimization problems: function evaluation, image clustering, permutation flow shop scheduling, and data clustering. The test data were selected from real-life situations. The results demonstrate that the proposed algorithm competes well against existing algorithms.

关键词： Metaheuristic algorithm particle swarm optimization algorithm Whale optimization algorithm Whale particle optimization algorithm optimization problems

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Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartD期126卷

作者： Movafegh, Zahra Rezapour, Abdoreza Mehrastan Univ Dept Comp Engn Guilan Iran Islamic Azad Univ Dept Comp Engn Astaneh Ashrafieh Branch Guilan Iran

In the era of internet and big data, recommender systems are necessary to filter out useless information. Collaborative filtering (CF) is one of the most successful technique used in recommender systems, this technique suffers from a large number of users and items, or the scalability issue. In this paper, a new hybrid method based on K-means clustering (KM) and Singular Value Decomposition (SVD) which uses evolutionary algorithms is proposed to deal with scalability issue. On the one hand, KM optimized by particle swarm optimization (PSO) and denoising by Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to cluster users and reduce the number of comparisons between the target user and other users. On the other hand, SVD optimized by Genetic algorithm (GA) is used to reduce the number of items. The proposed method is assessed on three standard datasets and the results are compared with basic CF and other extended versions that use clustering algorithms, evolutionary algorithms and dimensionality reduction techniques. The results show that our proposed method performed better than other methods in terms of precision, recall and MAE, and the scalability problem was improved by reducing the time complexity. Also, the combined clustering method was optimized in terms of Davies-Bouldin and the Dunn's index compared to the basic clustering methods.

关键词： Recommender system Collaborative filtering Clustering Singular value decomposition particle swarm optimization algorithm Genetic algorithm

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A Hybrid Genetic and particle swarm optimization algorithms for Dynamic Facility Layout Problem with Multiple Transporters 15

A Hybrid Genetic and Particle Swarm Optimization Algorithms ...

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15th Iran International Industrial Engineering Conference (IIIEC)

作者： Pourhassan, Mohammad Reza Raissi, Sadigh Islamic Azad Univ South Tehran Branch Sch Ind Engn Tehran Iran

ISBN: (纸本)9781728117751

Nowadays, manufacturing plants should be agile to changes their production mix plan based on dynamic demands. Here, layout design significantly could impact on manufacturing efficiency. When the flows of materials between departments embed variability during the planning horizon, this problem is known as the dynamic facility layout problem (DFLP). This paper extends such problem with considering multiple transporters, which commonly are used for transportation tasks among facilities. Hence, we extended the classical DFLP objective function in such a way that could encounter total combined rearrangement, material handling and transporting costs. Firstly, the relevant mathematical model is presented and then hybrid metaheuristic algorithms based on particle swarm optimization (PSO) and genetic algorithm (GA) presented to solve such problem efficiently. To achieve reliable results, a Taguchi's design of experiments is applied to calibrate initial parameters. Also, a few small-sized problems are solved using the CPLEX software. Analysis of the results shows that the proposed hybrid PSO algorithms have good solution quality according to the objective function and CPU time rather than hybrid GA and proved the effectiveness of this algorithm on the set of test problems.

关键词： Dynamic facility layout particle swarm optimization algorithm Genetic algorithm Taguchi design of experiments

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Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model

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JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2023年第16期360卷 11414-11443页

作者： Kan, Xiu Fan, Yixuan Zheng, Jinjie Chi, Chi-hung Song, Wanqing Kudreyko, Aleksey Shanghai Univ Engn Sci Sch Elect & Elect Engn Shanghai 201620 Peoples R China Natl Univ Singapore Sch Comp Singapore 119275 Singapore Ufa Univ Sci & Technol Dept Gen Phys Zaki Valedi 32 Ufa 450076 Russia

With the growth of access to internal system resources, the insider threat problem is emerging and can bring immeasurable losses to enterprises. In order to detect the hidden threats and guide the formulation of enterprise management strategy, it is important to analyze and understand the employee behavior. Thus, we suggest a user behavior analysis system framework, which mainly includes data processing, user behavior modeling and results analysis. Data Adjusting (DA) strategy and optimized eXtreme Gradient Boosting (XGBoost) model are utilized with the aim of full analysis small amount of feature information. The strategy for detecting suspicious behavior can be the following. Firstly, select initial suspicious data, misclassification data retention and combination sampling. Secondly, for further behavior model construction, an improved particle swarm optimization algorithm based on ethnic randomized particles (ERPSO), which introduces Gaussian white noise with adjustable intensity into acceleration coefficients is given for searching the optimal XGBoost parameters. In addition, based on the designed DA strategy and the proposed ERPSO algorithm, we have also compared the results of the proposed methods with the current state-of-the-art methods. Experimental results show that the XGBoost optimized by the ERPSO (ERPSO-XGBoost) model has comprehensive performance, which proves the rationality and effectiveness of the insider behavior analysis system framework. Through a comprehensive understanding of insider behavior, the obvious characteristic behavior is found to adjust the management strategy and guide the behavior modeling, so as to prevent more losses in time. (c) 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.

关键词： Insider threat Behavior modeling particle swarm optimization algorithm eXtreme Gradient Boosting Management strategy

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Control of thermal uniformity in microwave heating process by BPNN and adaptive particle swarm optimization

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Heliyon 2024年第21期10卷 e37971页

作者： Yao, Qing Zheng, Xin Wang, Rui Liang, Wenlong Liu, Tie Chu, Wenxiao Key Laboratory of Healthy & Intelligent Kitchen System Integration of Zhejiang Province Ningbo 315336 China Ningbo Fotile Kitchen Ware Company Zhejiang Ningbo 315336 China Key Laboratory of Thermal-Fluid Science and Engineering Ministry of Education Xi'an Jiaotong University Shaanxi Xi'an 710049 China

As China advances its green economy, innovative methods are being employed to enhance energy optimization and conservation within energy-intensive industries. Among these methods, microwave heating stands out due to its superior heating efficiency and lack of pollution. Nonetheless, uniform heating remains a challenge because the microwave absorption capacity of the heated medium varies with changes in heating time and temperature. To address this issue, an Adaptive particle swarm optimization (APSO) neural network microwave system based on the Back Propagation Neural Network (BPNN) is proposed. This system leverages the fundamental principles of particle swarm optimization (PSO), categorizing particle swarms into three types and iterating them through distinct processes to achieve optimal results. An APSO controller is designed based on system identification, adjusting the controller parameters according to the error between the real-time system output and the identification model. The feedback error is used as the fitness function in the PSO algorithm, continuously adjusting the weights and thresholds of the neural network. This intelligent control approach optimizes the microwave oven's input power to minimize the error between the actual temperature output and the identified temperature. The APSO controller is designed based on system identification, with the intelligently controlled microwave heating system adjusting its input power to minimize the error between the identified and actual temperature outputs. Unlike traditional Proportional Integral Differential (PID) and BPNN controllers, this approach calculates the output of the identified model and the error of the actual model, feeding this information back to the controller. The feedback error serves as the fitness function in the PSO algorithm, enabling continuous adjustment of the network's weights and thresholds to regulate the microwave equipment's output power, thereby ensuring the output temperature

关键词： APSO controller Back propagation neural network Control system Microwave heating particle swarm optimization algorithm

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Research on data mining algorithm based on neural network and particle swarm optimization

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018年第3期35卷 2921-2926页

作者： Fei, Xianju Tian, Guozhong Changzhou Inst Technol Sch Comp Informat & Engn Changzhou Jiangsu Peoples R China

In previous studies, due to the sparsity and chaos of distributed data, such a result would lead to a local convergence phenomenon by using PSO algorithm, resulting in low accuracy of data mining. So this time we proposed a data mining algorithm based on neural network and particle swarm optimization. At the beginning, we calculated the global kernel function of differentiated distributed data mining and mixed to build the mining decision model. The training error was used as the constraint condition of mining optimization to realized data optimization mining. The results showed that the differential distributed data mining with this algorithm has higher accuracy and stronger convergence.

关键词： Neural network algorithm particle swarm optimization algorithm data mining algorithm

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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第6期39卷 9027-9035页

作者： Chen, Xi Zhoukou Normal Univ Sch Mech & Elect Engn 9 Wenchang St Chuanhui Dist Zhoukou Peoples R China

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with particle swarm optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

关键词： Wind turbine fault diagnosis particle swarm optimization algorithm BP neural network

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