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Conference on Passive and Active MillimeterWave Imaging XXIII

作者： Molebny, Vasyl Sokurenko, Vyacheslav Qiu, Jinghui Acad Technol Sci Ukraine Kiev Ukraine NTUU Igor Sikorski Kyiv Polytech Inst Kiev Ukraine Harbin Inst Technol Harbin Peoples R China

ISBN: (数字)9781510636002

ISBN: (纸本)9781510636002

Free-form two-mirror antenna for millimeter wave imaging has been modeled using a proprietary software for optical systems design PODIL. Optimization procedure is based on adaptive Cauchy differential evolution method, the principal features of which allow efficient global optimization. The system of two mirrors has the 300 mm aperture stop, and both mirrors have 380 mm the larger size. Their profiles are described by the 7th order Zernike polynomials. Pick-to-valley height difference is 18.037 mm and 35.60 mm for primary and secondary mirror correspondingly. The system allows 20-degree field of view. The RMS of the focal plane spot diagram is balanced with variations within 0,54 ... 0.64 mm. 90% of energy in the focal plane over the total field of view is contained within the quadratic zones with the sides 1.6 mm. The antenna can have a focusing function.

关键词： millimeter wavelength imaging free-form mirrors free-form antenna reflectors global optimization adaptive Cauchy method differential evolution algorithm Zernike polynomials

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An Ensemble Approach for Extended Belief Rule-Based Systems with Parameter Optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第2期12卷 1371-1381页

作者： Huang, Hong-Yun Lin, Yan-Qing Su, Qun Gong, Xiao-Ting Wang, Ying-Ming Fu, Yang-Geng Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Fuzhou Univ Coll Econ & Management Fuzhou 350116 Peoples R China Wuchang Univ Technol Coll Business Wuhan 430223 Peoples R China

The reasoning ability of the belief rule-based system is easy to be weakened by the quality of training instances, the inconsistency of rules and the values of parameters. This paper proposes an ensemble approach for extended belief rule-based systems to address this issue. The approach is based on the AdaBoost algorithm and the differential evolution (DE) algorithm. In the AdaBoost algorithm, the weights of samples are updated to allow the new subsequent subsystem to pay more attention to those samples misclassified by pervious system. And the DE algorithm is used as the parameter optimization engine to ensure the reasoning ability of the learned extended belief rule-based sub-systems. Since the learned sub-systems are complementary, the reasoning ability of the belief rule-based system can be boosted by combing these sub-systems. Some case studies about many classification test datasets are provided in this paper in the last. The feasibility and efficiency of the proposed approach has been proven by the experimental results. (C) 2019 The Authors. Published by Atlantis Press SARL.

关键词： Extended belief rule base AdaBoost differential evolution algorithm

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Hybrid Particle Swarm Optimization with differential evolution for Numerical and Engineering Optimization

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International Journal of Automation and computing 2018年第1期15卷 103-114页

作者： Guo-Han Lin Jing Zhang Zhao-Hua Liu College of Electrical and Information Engineering Hunan University College of Electrical and Information Hunan Institute of Engineering School of Information and Electrical Engineering Hunan University of Science and Technology

In this paper, a hybrid particle swarm optimization （PSO） algorithm with differential evolution （DE） is proposed for numerical benchmark problems and optimization of active disturbance rejection controller （ADRC） parameters. A chaotic map with greater Lyapunov exponent is introduced into PSO for balancing the exploration and exploitation abilities of the proposed algorithm. A DE operator is used to help PSO jump out of stagnation. Twelve benchmark function tests from CEC2005 and eight real world opti- mization problems from CEC2011 are used to evaluate the performance of the proposed algorithm. The results show that statistically, the proposed hybrid algorithm has performed consistently well compared to other hybrid variants. Moreover, the simulation results on ADRC parameter optimization show that the optimized ADRC has better robustness and adaptability for nonlinear discrete-time systems with time delays.

关键词： Particle swarm optimization （PSO） active disturbance rejection control （ADRC） differential evolution algorithm chaoticmap parameter tuning.

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Position control for planar four-link underactuated manipulator with a passive third joint

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ISA TRANSACTIONS 2019年 87卷 46-54页

作者： Liu, Dong Lai, Xuzhi Wang, Yawu Wan, Xiongbo Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China

This paper presents a position control strategy based on the differential evolution (DE) algorithm for a planar four-link underactuated manipulator (PFUM) with a passive third joint, which is to move its end-point from any initial position to any target position. Based on the structural characteristic of the PFUM, a model reduction method is conceived to reduce the PFUM to a planar virtual three-link manipulator and a planar Acrobot in turn. Considering the existence of the angle constraint in the planar Acrobot, the DE algorithm is used to optimize and coordinate the control objective of each reduced system, and also to ensure the target angles of the planar Acrobot corresponding to the target position of the PFUM can be found. Simulations demonstrate the validity of the proposed control strategy. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Planar underactuated manipulator Nonholonomic system Position control Model reduction differential evolution algorithm

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Design optimization of a pattern reconfigurable microstrip antenna using differential evolution and 3D EM simulation-based neural network model

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2019年第8期29卷 e21796-e21796页

作者： Mahouti, Peyman Yildiz Tech Univ Elect & Commun Engn Dept Istanbul Turkey

In this work, design optimization of a varicap diode loaded antenna consisting of four identical rectangular microstrips is presented as a pattern reconfigurable antenna at 5.2 GHz. The microstrips are printed on the front of a FR4 substrate with the dimensions of 40 mm x 25 mm and epsilon(r) = 4.6, h = 1.58 mm and probe-fed via a coupling using a rectangular microstrip line symmetrically placed between them. In first stage, S-11 of the antenna are obtained as its real and imaginary parts as continuous functions of geometry of the microstrip components within 3 to 7 GHz using multi-layer perceptron (MLP) trained and validated by 3D EM simulated data. In order to determine the most suitable (MLP) architecture and training algorithm, 20 different MLP architectures are tested. Then, S-11 are optimized with respect to the geometry parameters using differential evolution algorithm and MLP based model. The antenna is prototyped with the optimally selected parameters and measured. From the comparison of simulation and measurement results, it can be observed that the measurement results agree with the simulation results, thus it can be concluded that the proposed antenna is a simple and successful design subject to the design purposes with together its design methodology.

关键词： ANN design optimization differential evolution algorithm pattern steering reconfigurable antenna

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Motion planning and adaptive neural sliding mode tracking control for positioning of uncertain planar underactuated manipulator

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NEUROCOMPUTING 2019年 334卷 197-205页

作者： Zhang, Pan Lai, Xuzhi Wang, Yawu Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Hubei Peoples R China Hubei Key Lab Adv Control & Intelligent Automat C Wuhan 430074 Hubei Peoples R China Concordia Univ Dept Mech Ind & Aerosp Engn Montreal PQ H3G 1M8 Canada

The research on the robust control of uncertain planar underactuated manipulators is almost nonexistent due to the lack of actuator and the uncontrollability at equilibrium points. Taking an uncertain planar three-link passive-active-active underactuated manipulator as an example, this paper develops a robust control scheme including motion planning and adaptive tracking control to realize its position control. According to the constraints between the passive link and active links and the target position of the manipulator, differential evolution algorithm is used to solve the target angles and ratio between the angular velocities. Then, a set of motion trajectories is planned based on the target values. Considering the uncertain parameter perturbation and external disturbance exist, we use RBF neural network to online approximate the uncertainty. Meanwhile, we develop a set of fast terminal sliding mode controllers to track the planned trajectories, and design adaptive laws to guarantee the stability and convergence of the tracking system. Next, an online iteration algorithm is presented to correct the deviations of all link angles caused by the parameter perturbation, which makes the manipulator gradually approach to its target position. Finally, the simulation results verify the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Planar underactuated manipulator Robust control Motion planning and tracking RBF neural network Adaptive terminal sliding mode control differential evolution algorithm

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Modified differential evolution approach for practical optimal reactive power dispatch of hybrid AC-DC power systems

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APPLIED SOFT COMPUTING 2018年 73卷 591-606页

作者： Sayah, Samir Ferhat Abbas Univ Dept Elect Engn QUERE Lab Setif 1 Algeria

Optimal reactive power dispatch (ORPD) is one of the most important functions of modern energy management systems, used to ensure stable and secure operating conditions of power networks. Currently, High voltage direct current (HVDC) transmission systems are widely used in modern electrical power systems due to their economic and technical advantages. The incorporation of DC link introduces more complexity in ORPD computation. In this paper, an efficient enhanced differential evolution algorithm with new mutation strategy is proposed to solve the ORPD problem incorporating a two terminal HVDC transmission link. To the best of the author's knowledge, no one has investigated the performance of DE on ORPD task. The performance of the proposed strategy has been investigated on two hybrid AC-DC systems, which are the modified New England 39-bus test system, and the practical and large-scale Algerian 114-bus electric network. To illustrate the effectiveness and potential of the proposed approach, the achieved results were evaluated and compared to those reported recently in literature. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Optimal power flow Optimal reactive power dispatch HVDC link AC-DC power system differential evolution algorithm

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Robust multi-user detection based on hybrid Grey wolf optimization

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2021年第15期33卷 e5273-e5273页

作者： Sun, Xiyan Fan, Zhuo Ji, Yuanfa Wang, Shouhua Yan, Suqing Wu, Sunyong Fu, Qiang Ghazali, Kamarul Hawari Guilin Univ Elect Technol Guangxi Key Lab Precis Nav Technol & Applicat Guilin 541004 Peoples R China Univ Malaysia Pahang Fac Elect & Elect Engn Pekan Malaysia

The search for an effective nature-inspired optimization technique has certainly continued for decades. This work proposes a novel robust multi-user detection algorithm based on Grey wolf optimization and differential evolution algorithm to overcome the problem of high bit error rate (BER) in multi-user detection under an impulse noise environment. The simulation results show that the iteration times of the multi-user detector based on the proposed algorithm is less than those of genetic algorithm, differential evolution algorithm, Grey wolf optimization algorithm, salp swarm algorithm, grasshopper optimisation algorithm, and whale optimization algorithm with the lowerst BER value.

关键词： differential evolution algorithm Grey wolf optimization algorithm hybrid optimization multi-user detection impulse noise

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Research and Improvement of Alpha-Beta Search algorithm in Gobang

Research and Improvement of Alpha-Beta Search Algorithm in G...

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作者： Yuan XIE Wenliang GAO Zuxu DAI Yuanyuan LI School of Mathematics and Phisics Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition

*** allusion to the Alpha-Beta search algorithm which is widely used in Gobang intelligent algorithm,this paper presents an improved method,that is,the heuristic search method using static table,which reduces the search *** the differential evolution algorithm is used to optimize the chess shape parameters and the static table *** to the simulation data,both methods improve the efficiency of Alpha-Beta search algorithm.

关键词： Gobang machine game Alpha-Beta search differential evolution algorithm

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Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Empirical Model and Improved Least Squares Support Vector Machine

Remaining Useful Life Prediction for Lithium-Ion Batteries B...

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2021中国智能自动化大会(CIAC 2021)

作者： Wan Chen Yanping Cai Aihua Li Yanzhao Su Ke Jiang High-Tech Institute of Xi'an

Remaining useful life(RUL) prediction for lithium-ion batteries is important for the safety of the battery. To improve the accuracy of multi-step prediction, a novel method based on empirical model and improved least squaressupport vector machine(LS-SVM) is proposed. Firstly, we constructed the deviation capacity based on the third-order polynomial empirical model to reduce the fluctuation of the data. Secondly, the differential evolution(DE) algorithm and multi-kernel(MK) functions were used to optimize the LS-SVM, and the improved LS-SVM was then built, which improved the prediction accuracy of the LS-SVM. On this basis, the framework for RUL prediction was constructed successfully. Finally, the experimental verification was carried out. The results show that the prediction accuracy of the proposed method is better than that of the existing methods.

关键词： Lithium-ion battery Remaining useful life prediction Multi-kernel least squares support vector machine differential evolution algorithm

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