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Lyapunov Control of High-Dimensional Closed Quantum Systems Based on particle swarm optimization

IEEE ACCESS 2020年 8卷 49765-49774页

作者： Guan, Xiaoke Kuang, Sen Lu, Xiujuan Yan, Jiazhen Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China Univ Sci & Technol China CAS Key Lab Technol Geospatial Informat Proc & Ap Hefei 230027 Peoples R China

For high-dimensional closed quantum systems, this paper proposes a novel quantum Lyapunov control scheme based on the particle swarm optimization algorithm and achieves a high-probability population transfer of the system to a non-isolated target eigenstate in the LaSalle invariant set under usual smooth Lyapunov control laws. Via a quadratic Lyapunov function with unknown parameters, a control law with the unknown parameters is designed;based on the LaSalle invariance principle and the energy-level connectivity graph, the stability of the system is analyzed;by using the particle swarm optimization algorithm, a set of optimal parameters is obtained to achieve the control goal. In particular, we propose a path planning method based on the energy-level connectivity graph to determine the initial values of the unknown parameters, which is such that the optimization algorithm can efficiently and conveniently find a set of optimal solutions of the unknown parameters. Numerical simulation experiments on a five-level quantum system and a three-qubit system demonstrate that the proposed Lyapunov control scheme based on the particle swarm optimization algorithm has a good control effect.

关键词： Lyapunov methods Sociology Statistics particle swarm optimization Control systems Quantum entanglement High-dimensional quantum systems Lyapunov control particle swarm optimization algorithm parameter optimization initial values energy-level connectivity graph

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particle-swarm-optimization-Based 2D Output Feedback Robust Constraint Model Predictive Control for Batch Processes

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IEEE ACCESS 2022年 10卷 8409-8423页

作者： Zhang, Wangxi Ma, Jian Wang, Liming Jiang, Feng Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Hainan Normal Univ Minist Educ Key Lab Data Sci & Intelligence Educ Haikou 571158 Hainan Peoples R China Key Lab Computat Sci & Applicat Hainan Prov Haikou 571158 Hainan Peoples R China Nanjing Univ Inst Philosophy Intelligence & Future Humanity Nanjing 210093 Peoples R China Nanjing Sport Inst Sch Phys Educ & Humanities Nanjing 210014 Peoples R China

For the input and output constraints and uncertainties in batch processes, a 2D output feedback robust constrained model predictive control (MPC) method is designed by combining iterative learning control (ILC), MPC and output feedback. Firstly, an equivalent 2D-FM closed-loop prediction model is established by combining with the proposed output feedback controller. Then an optimization performance index function with terminal constraint is constructed to study its control optimization. According to the designed optimization performance index and Lyapunov stability theory, the feasible MPC problem is obtained by solving the linear matrix inequalities (LMIs). At the same time, the gain of the new output feedback control law is given to ensure that the performance index reaches the minimum upper bound under the constraints of input and output. In order to solve the manual adjustment problem of some parameters in the performance index function, the particle swarm optimization (PSO) algorithm is introduced, and a better solution is found near the controller by using the search optimization method. Finally, taking the injection molding process as an example and comparing with the existing method without using PSO algorithms, it is proved that the above method is more feasible.

关键词： Output feedback Batch production systems Predictive control Performance analysis Uncertainty Prediction algorithms particle swarm optimization Batch processes input-output constraint iterative learning output feedback predictive control particle swarm optimization algorithm

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A Proposed Feature Selection particle swarm optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

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Computers, Materials & Continua 2024年第6期79卷 4081-4105页

作者： Yasser Hachaichi Ayman E.Khedr Amira M.Idrees College of Computing and Information Technology at Khulais Department of Information SystemsUniversity of JeddahJeddah21959Saudi Arabia Faculty of Computers and Information Technology Future University in EgyptCairo11835Egypt

The diversity of data sources resulted in seeking effective manipulation and *** challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of *** of the most successful optimization algorithms is particle swarm optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search *** research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO *** the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain *** proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant *** confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve ***,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different *** proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

关键词： optimization particle swarm optimization algorithm feature selection logistics supply chain management backlogs

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Phase Dependent and Independent Frequency Identification of Weak Signals Based on Duffing Oscillator via particle swarm optimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第1期33卷 223-239页

作者： Chang, Yuan Hao, Yi Li, Chunwen Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Datang Microelect Technol Co Ltd Beijing 100094 Peoples R China

In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detected weak signal based on the Duffing oscillator. For this purpose, the variance of the Duffing oscillator's output is exploited, which has the property of multi-extremum single-maximum (MESM) distribution with the frequency of the periodic signal. The impact of signal's phase on the MESM distribution is discussed. When the signal's phase is known, the frequency of the signal can be directly identified as that with the maximal variance, which leads to a nonlinear optimization problem that can be solved by a particle swarm optimization (PSO) algorithm. When the phase is unknown, the pi/2-phase-shift method is to be exploited integrated with a PSO algorithm. It is shown that the frequency can be precisely and efficiently identified by this method, whose effectiveness is verified by simulation results in Matlab.

关键词： Weak signal detection Duffing Chaotic Oscillator Frequency detection Variance particle swarm optimization algorithm

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Gene Regulatory Network Construction Based on a particle swarm optimization of a Long Short-term Memory Network

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CURRENT BIOINFORMATICS 2020年第7期15卷 713-724页

作者： Tang, Zhenhao Chai, Xiangying Wang, Yu Cao, Shengxian Northeast Elect Power Univ Sch Automat Engn Jilin Jilin Peoples R China

Background: The Gene Regulatory Network (GRN) is a model for studying the function and behavior of genes by treating the genome as a whole, which can reveal the gene expression mechanism. However, due to the dynamics, nonlinearity, and complexity of gene expression data, it is a challenging task to construct a GRN precisely. And in the circulating cooling water system, the Slime-Forming Bacteria (SFB) is one of the bacteria that helps to form dirt. In order to explore the microbial fouling mechanism of SFB, constructing a GRN for the fouling-forming genes of SFB is significant. Objective: Propose an effective GRN construction method and construct a GRN for the fouling-forming genes of SFB. Methods: In this paper, a combination method of Long Short-Term Memory Network (LSTM) and Mean Impact Value (MIV) was applied for GRN reconstruction. Firstly, LSTM was employed to establish a gene expression prediction model. To improve the performance of LSTM, a particle swarm optimization (PSO) was introduced to optimize the weight and learning rate. Then, the MIV was used to infer the regulation among genes. In view of the fouling-forming problem of SFB, we have designed electromagnetic field experiments and transcriptome sequencing experiments to locate the fouling-forming genes and obtain gene expression data. Result: In order to test the proposed approach, the proposed method was applied to three datasets: a simulated dataset and two real biology datasets. By comparing with other methods, the experimental results indicate that the proposed method has higher modeling accuracy and it can be used to effectively construct a GRN. And at last, a GRN for fouling-forming genes of SFB was constructed using the proposed approach. Conclusion: The experiments indicated that the proposed approach can reconstruct a GRN precisely, and compared with other approaches, the proposed approach performs better in extracting the regulations among genes.

关键词： Gene regulatory network long short-term memory particle swarm optimization algorithm mean impact value slime-forming bacteria yeast gene data machine learning

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An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第3期10卷 841-850页

作者： Qian, Bin Wang, Qian-Qian Hu, Rong Zhou, Zhi-Jie Yu, Chuan-Qiang Zhou, Zhi-Guo Kunming Univ Sci & Technol Coll Informat Engn & Automat Dept Automat Kunming 650500 Yunnan Peoples R China Rocket Force Univ Engn Xian 710025 Shanxi Peoples R China High Tech Inst Xian Xian 710025 Shanxi Peoples R China Univ Texas Southwestern Med Ctr Dallas Dept Radiat Oncol Dallas TX 75235 USA

This paper proposes a soft computing technology based on belief rule base (BRB) system for the tipping paper permeability measurement in tobacco factory. In current studies about BRB, both the referential values of the antecedent attributes and the utilities of the consequents are given in advance and are not trained by using the dedicated optimal algorithms. The limitations of expert knowledge may lead to error of BRB because both the referential values and the utilities make a real difference on the structure of BRB, and the appropriate structure is helpful for tuning parameters more accurately. Therefore, this paper focuses on the structure and parameters optimization of BRB (SPO-BRB) by taking the referential values of the antecedent attributes and the utilities of the consequents into account to improve the input-output modeling ability of BRB. However, SPO-BRB is a nonlinear nonconvex optimization problem (NNOP). To deal with the NNOP of SPO-BRB, a particle swarm optimization algorithm with improved velocity update way and repair methods (PSO_VR) is proposed. A case study based on the data collected from a tobacco factory of china is carried out. The test results demonstrate the functionality of SPO-BRB and the effectiveness of PSO_VR.

关键词： Soft computing technology Belief rule base Parameter and structure identification particle swarm optimization algorithm Porosity measuring

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Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

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APPLIED ACOUSTICS 2014年第0期80卷 57-67页

作者： Hajihassani, M. Armaghani, D. Jahed Sohaei, H. Mohamad, E. Tonnizam Marto, A. Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Utm Skudai 81310 Johor Malaysia

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Quarry blasting Airblast-overpressure Artificial neural networks particle swarm optimization algorithm

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Multi-Objective Optimum Design of High-Speed Backplane Connector Using particle swarm optimization

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IEEE ACCESS 2018年 6卷 35182-35193页

作者： Yu, Wenjie Zeng, Zhi Peng, Bei Yan, Shuo Huang, Yueshuang Jiang, Hai Li, Xunbo Fan, Tao Univ Elect Sci & Technol China Sch Mechatron Engn Chengdu 611731 Sichuan Peoples R China UESTC Guangdong Inst Elect & Informat Engn Guangzhou 23808 Guangdong Peoples R China

This paper outlines a new procedure for computer modeling and optimum design for the dynamic mechanical and electrical study of a high-speed backplane connector, which is a key electrical interconnection technology in large communications equipment, ultra-high performance servers, supercomputers, industrial computers, high-end storage devices, and so on. The optimum structure design of contact pairs is important for a backplane connector in meeting multiple challenges in terms of minimizing the maximum insertion force and the contact resistance. Current optimization schemes, such as the quadrature method, are relatively complex. Therefore, we designed the connector contact pairs for simultaneously obtaining the proper insertion force and the contact resistance through a multi-objective particle swarm optimization (MCDPSO) method with simpler settings and faster convergence speed. In this paper, the required insertion force was minimized during the entire process, and the minimum contact resistance was maintained after insertion. To this end, an MCDPSO algorithm was proposed for the connector design. A dynamic weight coefficient was developed to calculate the interval values of the reserved solutions for the selection of the operator, and an external archive update based on roulette wheel selection and gbest selection strategies was developed to increase the diversity of the solutions. A set of optimal structure solutions of the contact pairs was obtained for the subsequent design optimization. The feasibility and effectiveness of the proposed method were verified by comparing with the results from ANSYS finite element simulation.

关键词： High-speed backplane connector contact pairs insertion force contact resistance multi-objective connector design particle swarm optimization algorithm

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Prediction of yarn strength based on an expert weighted neural network optimized by particle swarm optimization

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TEXTILE RESEARCH JOURNAL 2021年第23-24期91卷 2911-2924页

作者： Zhang, Baowei Song, Jiuxiang Zhao, Suna Jiang, Hao Wei, Jingdian Wang, Yonghua Zhengzhou Univ Light Ind Zhengzhou 450002 Henan Peoples R China

Aiming at solving the problem that existing artificial neural networks (ANNs) still have low accuracy in predicting yarn strength, this study combines traditional expert experience and an ANN to propose a hybrid network, named the expert weighted neural network. Many studies have shown that it is reliable to predict yarn strength based on ANN technology. However, most ANN training models face with problems of low accuracy and easy trapping into their local minima. The strength prediction of traditional yarns relies on expert experience. Obvious expert experience can help the model perform preliminary learning and help the algorithm model achieve higher accuracy. Therefore, this study proposes a neural network model that combines expert weights and particle swarm optimization (PSO). The model uses PSO to optimize the weights of experts and investigates its effectiveness in yarn strength prediction.

关键词： Yarn strength expert weights artificial neural network expert weighted neural network particle swarm optimization algorithm

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The Influence of particle swarm optimization-Back Propagation Neural Network Hyperparameter Selection on the Prediction Accuracy of Converter Endpoint Temperature

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STEEL RESEARCH INTERNATIONAL 2024年第10期95卷

作者： Xin, Tongze Wang, Min Li, Yihong Univ Sci & Technol Beijing State Key Lab Adv Met Beijing 100083 Peoples R China Univ Sci & Technol Beijing Tech Support Ctr Prevent & Control Disastrous Acci State Key Lab Adv Met Beijing 100083 Peoples R China Taiyuan Univ Sci & Technol Coll Mat Sci & Engn Taiyuan 030024 Peoples R China

The converter is a complex, high temperature, high pressure reactor with limited internal moitoring. At present, data-driven models mainly focus on the prediction differences between algorithms, and there is relatively little analysis of the impact of different hyperparameters on prediction accuracy. Taking a 120 t converter in a Chinese steel plant as an example, this paper explores the application of particle swarm optimization-back propagation neural network (PSO-BP) in converter temperature prediction. First, the Pauta criterion or Box plot method was used to preprocess the data by prescreening. Subsequently, the influence of the activation function, learning rate, and number of hidden layer nodes of BP on the prediction accuracy of the endpoint temperature were explored. Then we investigated the influence of PSO parameters on the optimal result of BP initial value. Comparing the temperature prediction hit rate before and after optimization, the BP model has hit rates of 63.64%, 79.22%, and 87.45% within +/- 10, +/- 15, and +/- 20 degrees C, respectively, and the PSO-BP model has hit rates of 68.40%, 84.85%, and 94.81%, respectively. In comparison, PSO-BP extracts data features more accurately, has higher stability, and has better accuracy in predicting the endpoint temperature of the converter. This article establishes a particle swarm optimization-back propagation neural network (PSO-BP) model for predicting converter endpoint temperature, explores the influence of hyperparameters on the accuracy of PSO-BP prediction, reveals the principle of PSO for BP, and obtains the optimal parameter selection scheme for the model. Data validation confirms PSO-BP's effectiveness in extracting data features and achieving high prediction *** (c) 2024 WILEY-VCH GmbH

关键词： back propagation neural network converter endpoint prediction data driven hyperparameter analysis particle swarm optimization algorithm

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