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The Parameter Identification of PMSM Based on Improved cuckoo algorithm

NEURAL PROCESSING LETTERS 2019年第3期50卷 2701-2715页

作者： Wu, Zhongqiang Du, Chunqi Yanshan Univ Coll Elect Engn Qinhuangdao 066004 Hebei Peoples R China

In view of the multi-parameter identification problem of permanent magnet synchronous motor, a kind of parameter identification method was proposed based on an improved cuckoo search algorithm. cuckoo search algorithm has the advantages of simple, less parameters, fast convergence etc., but it also has the defects of premature convergence and low computation accuracy. In view of the deficiency of cuckoo search algorithm, the fuzzy reasoning based on the cloud membership was designed to adjust the probability of an alien egg being discovered by host birds and adaptive variable step method was used to adjust the step size of Levy flights. The improved algorithm can accelerate the convergence speed and improve the local and global optimizing ability by increasing the diversity of the population. The multi-parameter identification results of permanent magnet synchronous motor show that the improved cuckoo algorithm can effectively identify the motor parameters, and compared with the traditional cuckoo algorithm, the effectiveness and superior performance are tested.

关键词： Permanent magnet synchronous motor Cloud membership cuckoo algorithm Parameter identification Fuzzy

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Thermal error modeling of electric spindles based on cuckoo algorithm optimized Elman network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第3-4期132卷 1365-1375页

作者： Dai, Ye Wang, Xin Li, Zhaolong He, Sai Yu, Baolei Zhou, Xingwen Harbin Univ Sci & Technol Key Lab Adv Mfg Intelligent Technol Minist Educ Harbin 150080 Peoples R China Harbin Univ Sci & Technol Inst Digital Design & Automat Machinery Prod Dev Harbin 150080 Peoples R China

In order to improve the accuracy of the thermal error model of the electric spindle, a thermal error modeling method based on the optimized Elman neural network using the cuckoo algorithm is proposed. To analyze the thermal behavior of the electric spindle, an ANSYS analysis approach is utilized to create a temperature map. Based on the simulation analysis outcomes, an experimental platform is established to gather temperature data and thermal displacement data. The electric spindle temperature is optimized through the utilization of fuzzy cluster analysis and the Spearman rank correlation coefficient method in combination. The comparison between the established model and the Elman model and the GA-Elman model proves that the CS-Elman model has good prediction accuracy and stability.

关键词： cuckoo algorithm Elman neural network Thermal displacement Spearman rank correlation coefficient method Thermal error modeling

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Addressing the Cold-Start Problem Using Data Mining Techniques and Improving Recommender Systems by cuckoo algorithm: A Case Study of Facebook

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COMPUTING IN SCIENCE & ENGINEERING 2020年第4期22卷 62-73页

作者： Forouzandeh, Saman Aghdam, Atae Rezaei Forouzandeh, Soran Xu, Shuxiang Univ Appl Sci & Technol Dept Comp Engn Tehran Iran Ctr Tehran Municipal ICT Org Tehran Iran Univ Appl Sci & Technol Informat Technol Dept Tehran Iran Univ Appl Sci & Technol Tehran Iran Univ Tasmania Sch Technol Environm & Design Hobart Tas Australia

One of the most common problems in recommender systems is a "cold-start" problem, which is related to users who do not indicate any behavior on social media. This paper proposes a solution for tackling this ... 详细信息

关键词： Facebook Recommender systems Data mining Clustering algorithms Optimization Software Recommender Systems Data Mining Social Networks Cold-start cuckoo algorithm

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A process prediction model based on cuckoo algorithm for abrasive waterjet machining

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JOURNAL OF INTELLIGENT MANUFACTURING 2015年第6期26卷 1247-1252页

作者： Mohamad, Azizah Zain, Azlan Mohd Bazin, Nor Erne Nazira Udin, Amirmudin Univ Teknol Malaysia Fac Comp Soft Comp Res Grp Skudai 81310 Kagawa Malaysia Univ Teknol Malaysia Fac Educ Skudai 81310 Kagawa Malaysia

cuckoo is a new evolutionary algorithm which is inspired by lifestyle of bird family. This study proposes cuckoo algorithm for prediction of the surface roughness of Abrasive Water Jet (AWJ). Several prediction models with different initial eggs were developed and analyzed to investigate the best predicted surface roughness value. The paired sample t-test was used to demonstrate the validity of the results. Throughout this study, it was evidence that the cuckoo algorithm could improve machining performances of the AWJ. The result shows that the more initial eggs were considered, a much lower predicted value of surface roughness was obtained. When the initial eggs increase, the value of the best parameters become nearer to the goal point. It was found that cuckoo algorithm is capable for giving an improved surface roughness as it outperformed the results of two established computational techniques, artificial neural network and support vector machine.

关键词： cuckoo algorithm Surface roughness Abrasive water jet Machining

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Sensitive Vector Search for Logic Circuit Failure Probability based on Improved Adaptive cuckoo algorithm

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JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE 2022年第2期22卷 69-83页

作者： Cai, Shuo Wu, Sicheng Wang, Weizheng Yu, Fei Yin, Lairong Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 HN Peoples R China Changsha Univ Sci & Technol Sch Automot & Mech Engn Changsha 410114 HN Peoples R China

With the development of microelectronics technology, the feature size of integrated circuit continues to shrink, and circuit performance has been improved. At the same time, however, factors such as process disturbance, power noise, and particle radiation are having an increasingly serious influence on the Failure Probability of Circuits (FPC). Searching the input vectors that are sensitive to FPC can assist circuit designers in selectively reinforcing the circuit to reduce the fault-tolerant overhead and improve the fault-tolerant efficiency. In this paper, an Improved Adaptive cuckoo Search (IACS) algorithm is proposed to search sensitive circuit vectors. The vector segmentation strategy is used to change the dimension of the input vector, the hill climbing algorithm is used to improve the quality of the initial population, and the adaptive strategy is used to control parameters such as power-law index, discovery probability and scaling factor. At the same time, a Correlation Separation Approach (COSEA) is proposed to calculate the FPC under specific vector excitation. Experimental results show that the proposed algorithm has higher accuracy and better efficiency compared with existing algorithms.

关键词： Failure probability cuckoo algorithm sensitivity vector logical circuit semiconductors

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A novel estimation method for capillary pressure curves based on routine core analysis data using artificial neural networks optimized by cuckoo algorithm - A case study

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FUEL 2018年 220卷 363-378页

作者： Jamshidian, Majid Zadeh, Mostafa Mansouri Hadian, Mohsen Moghadasi, Ramin Mohammadzadeh, Omid Islamic Azad Univ Omidiyeh Branch Dept Petr Engn Omidiyeh Iran Petr Univ Technol Dept Automat & Instrumentat Engn Ahvaz Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran DBR Technol Ctr Schlumberger Edmonton AB Canada Schlumberger Doll Res Ctr Cambridge MA 02139 USA

Capillary pressure is one of the most important parameters affecting fluids distribution in a reservoir rock and is an essential input parameter for reservoir simulation. Measurement of capillary pressure data, in the context of special core analysis (SCAL) is a time and cost consuming process that does not often lead to accurate and reliable results. Routine core analysis (RCAL) data, on the other hand, can be obtained by simple, accurate, and cost-effective procedures. In this paper, the idea of using RCAL measurements to predict SCAL data (more specifically capillary pressure data) using Artificial Neural Network (ANN) approach is presented. An ANN with Multi-Linear Perceptron structure and feed-forward propagation was used to predict capillary pressure curves for a target reservoir under study. The ANN model was then optimized by cuckoo optimization algorithm (COA). The ANN-COA model was used for 30 measurements, composed of both drainage and imbibition data points obtained from 15 core samples using centrifugation method. Out of this databank, 16 measurements were used for training and the remaining 14 measurements were used as the testing dataset. It was obtained that the optimized model shows a profound predicting performance based on the excellent value of coefficient of determination for predicted versus measured capillary pressure data.

关键词： Capillary pressure Artificial Neural Network cuckoo algorithm Routine core analysis Special core analysis

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Coverage and Routing Optimization of Wireless Sensor Networks Using Improved cuckoo algorithm

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IEEE ACCESS 2024年 12卷 39564-39577页

作者： Yang, Jian Xia, Yimin Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China

Wireless sensor networks have gradually attracted widespread attention from academia and industry due to their wide application potential and versatility. In response to the coverage and routing issues of wireless sensor networks, this study combines the improved cuckoo algorithm and non-uniform clustering algorithm to design a coverage and routing optimization scheme for wireless sensor networks. Firstly, a wireless sensor network system is studied and built. Subsequently, the traditional cuckoo algorithm is optimized using the Cauchy distribution, and a coverage optimization scheme for wireless sensor networks based on the Cauchy distribution improved cuckoo algorithm is designed. Finally, by optimizing the dynamic range of network area and cluster radius respectively to improve the shortcomings of non-uniform clustering routing algorithms, a wireless sensor networks routing optimization scheme based on dynamic cluster radius optimization non-uniform clustering algorithm is designed. The research results indicated that the designed coverage scheme and routing scheme achieved good application results. Among them, the designed coverage scheme had a minimum running time of 0.89 minutes in node coverage problems and a maximum node coverage rate of 99.52%. The designed routing scheme had a minimum running time of 1.47 minutes and an energy loss rate of 0.84% in the routing optimization problem. In summary, the coverage scheme and routing optimization scheme designed by this research institute have good application effects and can provide certain technical support for the application of wireless sensor networks in other fields.

关键词： Wireless sensor networks Signal integrity Signal processing Routing protocols Optimization methods Clustering algorithms Heuristic algorithms Energy loss Dynamic range algorithm design and analysis cuckoo algorithm wireless network sensors router signal coverage node

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Trajectory tracking of a quadrotor using a robust adaptive type-2 fuzzy neural controller optimized by cuckoo algorithm

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ISA TRANSACTIONS 2021年 114卷 171-190页

作者： Shirzadeh, Masoud Amirkhani, Abdollah Tork, Nastaran Taghavifar, Hamid Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran 158754413 Iran Iran Univ Sci & Technol Sch Automot Engn Tehran *** Iran Iran Univ Sci & Technol Sch Elect Engn Tehran *** Iran Coventry Univ Sch Mech Aerosp & Automot Engn Coventry CV1 2JH W Midlands England

This paper proposes an adaptive and robust adaptive control strategy based on type-2 fuzzy neural network (T2FNN) for tracking the desired trajectories of a quadrotor. The designed methods can control both the position and the orientation of a quadrotor. Contrary to common sliding mode controllers (SMCs), the robust adaptive trajectory tracking scheme presented here is based on SMC with exponential reaching law;which helps reduce the phenomenon of chattering. Moreover, parameters including the gains of sliding surfaces, are optimized by cuckoo optimization algorithm (COA), and the results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO). The designed methods in this study are called adaptive T2FNN controller and the exponential SMC (ESMC)-T2FNN. The law for updating the T2FNN is obtained online by using the Lyapunov stability theory. Considering undesired factors such as uncertainties, external disturbances and control signal saturation, the results of our controllers are compared with those of the adaptive type-1 fuzzy neural network controller (T1FNN) and ESMC-T1FNN. The extensive simulations demonstrate the effectiveness of the proposed COA-based ESMC-AT2FNN approach compared to the other tested techniques (i.e. GA, PSO and ACO) in terms of the improved transient and steady-state trajectory-tracking performance. The mean and standard deviation values concerning the COA are obtained through statistical analyses at 0.00006173 and 0.000092, respectively. This paper also examines the complexity of COA in optimizing the trajectory tracking control of quadrotor and investigates the effects of COA parameters on optimization results. The stable performance of the cuckoo algorithm is demonstrated by varying its parameters and analyzing the obtained results. These results also show the convergence of COA for the considered problem. (C) 2020 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Type-2 fuzzy controller Fuzzy neural network Quadrotor Trajectory tracking cuckoo algorithm

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Capacity Optimization Configuration for a Park-Level Hybrid Energy Storage System Based on an Improved cuckoo algorithm

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PROCESSES 2024年第4期12卷 718页

作者： Huang, Zhangchenlong Bei, Lei Wang, Ben Xu, Linlin Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Scotland Hubei Univ Econ Collaborat Innovat Ctr Emiss Trading Syst Coconstr Wuhan 430205 Peoples R China Huazhong Univ Sci & Technol State Key Lab Coal Combust & Low Carbon Utilizat Wuhan 430074 Peoples R China

To promote the development of green industries in the industrial park, a microgrid system consisting of wind power, photovoltaic, and hybrid energy storage (WT-PV-HES) was constructed. It effectively promotes the local consumption of wind and solar energy while reducing the burden on the grid infrastructure. In this study, the analytic hierarchy process (AHP) was used to decompose the multi-objective function into a single-objective function. The economic and environmental benefits of the system were taken as the objective function. Furthermore, the cuckoo search algorithm (CS) was used to solve the specific capacity of each distributed power source. Different scenarios were applied to study the specific capacity of microgrid systems. The results show that the equivalent annual cost of the WT-PV-HES microgrid system is reduced by 7.3 percent and 62.23 percent, respectively. The carbon disposal cost is reduced by 1.71 and 2.38 times, respectively. The carbon treatment cost is more sensitive to load changes. The solution iteration of the cuckoo algorithm is 18 times. Meanwhile, the system requires four updates of capacity allocation results for 20 years of operation. This result validates the effectiveness of the proposed model and methodology. It also provides a reference for the research and construction of capacity allocation of microgrid systems at the park level.

关键词： hybrid energy storage analytic hierarchy process cuckoo algorithm multi-objective optimization

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A RLC parameter identification method for synchronous buck converters based on simulated annealing coupled cuckoo algorithm

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ELECTRICAL ENGINEERING 2024年 1-14页

作者： An, Xiaoyu Zhao, Feng Dou, Zhifeng Lu, Falong Wang, Qian Zhengzhou Univ Light Ind Coll Elect & Informat Engn Zhengzhou 450002 Peoples R China

Fault detection technology based on parameter identification is advancing rapidly. For power electronic circuits, parameter identification is essential for accurately evaluating performance. The commonly used identification algorithms in engineering include least squares method and particle swarm optimization algorithm, etc. The least squares method has the disadvantage of large identification error and low identification accuracy, while the convergence instability and susceptibility to local optima of particle swarm optimization algorithm also need to be improved. To address the shortcomings of the least squares method and leverage the advantages of the cuckoo algorithm, such as strong global search capability and fast convergence, this paper focuses on the synchronous buck circuit. A local linear model of the synchronous buck circuit is established to mitigate the influence of nonlinear signals, and the signals collected include the terminal voltage VS2, inductor current iL and output voltage V0. The cuckoo algorithm is employed to identify the parameters. To overcome the local optimum problem associated with the cuckoo algorithm, an improved cuckoo algorithm is proposed that integrates simulated annealing to identify the parameters L, C, RL and RC in the circuit. To demonstrate the robustness and convergence of the proposed algorithm, we use the MATLAB/Simulink platform for comparative analysis against the conventional least squares method. The results show that the improved cuckoo algorithm achieves faster convergence and greater robustness. Finally, validation on a physical experimental platform indicates that the data extracted from the actual circuit maintains high identification accuracy and convergence speed, with identification errors below 5%, further verifying the effectiveness of the algorithm.

关键词： cuckoo algorithm Fault detection Identification accuracy Parameter identification Simulated annealing Synchronous buck

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