检索结果-内蒙古大学图书馆

5th International Conference on Computer Engineering and Application (ICCEA)

作者： Liu, Bowen Wu, Zhongcheng Anhui Univ Inst Phys Sci & Informat Technol Hefei 230031 Peoples R China Chinese Acad Sci High Magnet Field Lab HFIPS Hefei 230031 Peoples R China

ISBN: (纸本)9798350386783;9798350386776

A BP neural network evaluation model based on principal component analysis and particle swarm optimisation algorithm (PCA- PSO-BP) is proposed to address the problems of single selection indicators, excessive reliance on manual detection, and large evaluation error in the existing residual value evaluation of used new energy vehicles. Firstly, the principal component analysis method is used to screen out the most important feature indicators that affect the residual value of new energy vehicles. Then, the particle swarm optimization algorithm is used to optimize the weights and thresholds of the BP neural network. Finally, the selected features are input into the optimized BP neural network to output the residual value evaluation result. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current state of the art. The results indicate that the PCA-PSO-BP neural network residual value evaluation model has high prediction accuracy and can provide reference for the current residual value evaluation methods of second-hand new energy vehicles.

关键词： new energy vehicles residual value evaluation particle swarm optimisation algorithm bp neural network

来源：评论

学校读者我要写书评

暂无评论

Adaptive backstepping controller design on Buck converter with a novel improved identification method

引用

IET CONTROL THEORY AND APPLICATIONS 2022年第5期16卷 485-495页

作者： Saadat, Seyyed Amirhossein Ghamari, Seyyed Morteza Mollaee, Hasan Shahrood Univ Technol Fac Elect & Robot Engn Shahrood Semnan Iran

An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

关键词： system identification particle swarm optimisation algorithm high variance noises identification method least-squares identification algorithm robust dynamics parametric uncertainties PWM invertors robust control three-term control control structure adaptive backstepping controller design load variations power generation control particle swarm optimisation adaptive control PID controller improved identification technique adaptive mechanism DC-DC buck converter adaptive backstepping method Lyapunov methods control system synthesis DC-DC power convertors black-box block pulse width modulation

来源：评论

学校读者我要写书评

暂无评论

State-of-charge estimation for Lithium-Ion batteries using Kalman filters based on fractional-order models

引用

CONNECTION SCIENCE 2022年第1期34卷 162-184页

作者： Xing, Likun Ling, Liuyi Gong, Bing Zhang, Menglong Anhui Univ Sci & Technol Sch Elect & Informat Technol Huainan 232001 Peoples R China Anhui Univ Sci & Technol Sch Artificial Intelligence Huainan 232001 Peoples R China

The accuracy of state of charge estimation results will directly affect the performance of battery management system. Due to such, we focus in this article on the SOC estimation of Lithium-Ion batteries based on a fractional second-order RC model with free noninteger differentiation orders. For such an estimation, three Kalman filters are employed: the adaptive extended Kalman filter (AEKF), extended Kalman filter (EKF), and Unscented Kalman Filter (UKF). The Fractional-Order Model (FOM) parameters and differentiation orders are identified by the particle swarm Optimization (PSO) algorithm, and a pulsed-discharge test is implemented to verify the accuracy of parameter identification. The output voltage error of the FOM model is much less than that of the Integer-Order Model (IOM). The FOM model has lower root-mean square error (RMSE), the mean absolute error (MAE), and the maximum absolute error (MAXAE) of SOC estimation than the IOM model during the SOC estimation regardless of AEKF, EKF or UKF. Experimental results show that the FOM can simulate the polarisation on effect and charge-discharge characteristics of the battery more realistically, demonstrating that the SOC estimation based on FOM is more accurate and promising than the one based on the IOM when using the same Kalman filters.

关键词： Fractional calculus Lithium-Ion batteries state-of-charge particle swarm optimisation algorithm Kalman filter

来源：评论

学校读者我要写书评

暂无评论

Increasing the ampacity of underground cable lines by optimising the thermal environment and design parameters for cable crossings

引用

IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第11期16卷 2309-2318页

作者： Perovic, Bojan Klimenta, Dardan Tasic, Dragan Raicevic, Nebojsa Milovanovic, Milos Tomovic, Milan Vukasinovic, Jovan Univ Pristina Kosovska Mitrovica Fac Tech Sci Kneza Milosa St 7 RS-38220 Kosovska Mitrovica Serbia Univ Nis Fac Elect Engn Nish Serbia Kosovo & Metohija Acad Appl Studies Dept Zvecan Zvecan Serbia

The problem of optimising the thermal environment and design parameters of underground cable lines for cable crossings with the aim of increasing the ampacities of cables is considered in this paper. particle swarm optimisation (PSO) algorithm, formulated as a continuous non-linear optimisation problem with constraints, for solving this hot spot problem is applied. It is found, using the PSO algorithm, that there are a suitable size of cable bedding and an arrangement of cables within that bedding, which can eliminate or significantly mitigate the hot spot effect without the use of any additional cooling equipment. In this manner, the ampacities of both crossing cable lines increase by about 15% on average with respect to the case of a similar crossing with installation parameters commonly used. In addition, it is shown how the cross-sectional areas of the conductors and metal screens and the metal screen bonding methods affect the optimal solution.

关键词： thermal environment conductors (electric) design parameters particle swarm optimisation algorithm hot spot problem power cables underground cables particle swarm optimisation cable bedding hot spot effect installation parameters crossing cable lines cross-sectional areas PSO algorithm metal screen bonding methods underground cable lines cooling continuous nonlinear optimisation problem

来源：评论

学校读者我要写书评

暂无评论

Multi-objective robust optimisation model for MDVRPLS in refined oil distribution

引用

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2022年第22期60卷 6772-6792页

作者： Xu, Xiaofeng Lin, Ziru Li, Xiang Shang, Changjing Shen, Qiang China Univ Petr Sch Econ & Management Qingdao Peoples R China Beijing Univ Chem Technol Sch Econ & Management Beijing 100029 Peoples R China Aberystwyth Univ Fac Business & Phys Sci Dept Comp Sci Aberystwyth Dyfed Wales

At depots with refined oil shortage, arranging a reasonable distribution scheme with limited supply affects operation costs, demand satisfaction rate of gasoline stations (hereafter, 'station satisfaction'), and overtime penalty. This study considers the refined oil distribution problem with shortages using a multi-objective optimisation approach from the perspective of decision makers of oil marketing companies. The modelling and solving process involves (i) formulation of a crisp multi-depot vehicle routing model with limited supply (MDVRPLS) which considers station priority and soft time windows, (ii) development of a robust optimisation model (ROM) to manage uncertainty in demand, and (iii) the proposal of a multi-objective particle swarm optimisation (MOPSO)algorithm. Results of numerical experiments show that (i) the crisp model can better balance operation costs, station satisfaction, and overtime penalty, which produces 3.33% and 4.60% increase in station satisfaction at an increased unit cost and overtime penalty respectively;(ii) ROM successfully addresses uncertainty in demand compared to the crisp model, which requires an additional 8.81% in cost and 12.85% in penalty;and (iii) the MOPSO manages these MDVRPLS models more effectively than other heuristic algorithms. Therefore, applying ROM of refined oil supply shortage to the management significantly improves the efficiency and resists the disturbance caused by external uncertainties, providing scope for efficient distribution of scarce resources.

关键词： MDVRPLS refined oil distribution robust optimisation multi-objective optimisation particle swarm optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

Generalised model predictive controller design for A DC-DC non-inverting buck-boost converter optimised with a novel identification technique

引用

IET POWER ELECTRONICS 2022年第13期15卷 1350-1364页

作者： Ghamari, Seyyed Morteza Khavari, Fatemeh Molaee, Hasan Wheeler, Patrick Shahrood Univ Technol Semnan Fac Elect & Comp Engn Semnan Iran Univ Nottingham Fac Engn Nottingham England

An on-line generalised model predictive control (GMPC) strategy is designed and optimised with a novel identification procedure in the presence of different disturbances. The principle of MPC is utilising a discrete-time model of a system to reach the control variables with a prediction over these values, which is followed by computing a cost function for the control aims. Non-inverting buck-boost converter is a non-minimum phase system based on its boost mode, which makes a challenging condition for designing a stable controller. The proposed control technique described in this paper removes the requirement for a system mathematical model adopting a black-box identification method which can decrease the computational burden. Numerous harmful disturbances can affect a DC-DC converter;thus, the GMPC scheme is used along with a novel improved exponential regressive least identification algorithm as an adaptive strategy for the controller to optimise the gains of the controller in an on-line way resulting in better disturbance rejection. A PID controller with particle swarm optimisation algorithm is designed for this converter to be compared with the GMPC controller. Finally, the efficiency of the GMPC is verified in various performances with experimental and simulation results.

关键词： generalised model predictive controller design controller design stability three-term control discrete-time model black-box identification method nonminimum phase system predictive control DC-DC noninverting buck-boost converter DC-DC power convertors control system synthesis particle swarm optimisation identification DC-DC converter GMPC controller design exponential regressive least identification algorithm power system control particle swarm optimisation algorithm system mathematical model PID controller regression analysis

来源：评论

学校读者我要写书评

暂无评论

Area optimisation of Two Stage Miller Compensated Op-Amp in 65 nm Using Hybrid PSO

引用

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2022年第1期69卷 199-203页

作者： Rashid, Ria Nambath, Nandakumar Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India

Analog circuit design can be formulated as a nonlinear constrained optimisation problem that can be solved using any suitable optimisation algorithms. Different optimisation techniques have been reported to reduce the design time of analog circuits. A hybrid particle swarm optimisation algorithm with linearly decreasing inertia weight for the optimisation of analog circuit design is proposed in this study. The proposed method is used to design a two-stage operational amplifier circuit with Miller compensation. The results show that the proposed optimisation method can substantially reduce the design time needed for analog circuits.

关键词： Optimization Analog circuits Transistors Optimization methods Standards Circuits and systems Circuit synthesis Analog circuit design area optimization particle swarm optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

Bounded rational real-time charging pricing strategy under the traffic-grid coupling network

引用

IET ELECTRICAL SYSTEMS IN TRANSPORTATION 2022年第4期12卷 251-268页

作者： Wang, Chudi Ma, Shaohua Cai, Zhiyuan Yan, Ning Wang, Qiwei Shenyang Univ Technol Dept Elect Engn 111 Shenliao West Rd Shenyang Peoples R China

This study proposes a bounded rational charging guidance strategy based on mental account theory, which guides users to charge in an orderly manner by formulating real-time charging prices. Firstly, an orderly guidance framework for fast-charging EVs under the traffic-grid coupling network is constructed, and the influencing factors of various dimensions when users make charging decisions are analysed. Secondly, considering the bounded rational behaviour of users when making charging decisions, a multifactor bounded rational charging model for EV users based on mental account theory is proposed so as to obtain different charging costs for users when selecting charging stations. On this basis, a real-time charging price strategy based on the Stackelberg game model is constructed, with the goal of maximising the economic benefits of charging station operators while reducing the charging cost of EV users as much as possible. Finally, the particle swarm optimisation algorithm is used to solve the game model so as to solve the real-time charging price under various constraints. The simulation of an example verifies the rationality of the proposed real-time charging price formulation method and the superiority of the bounded rational charging guidance strategy.

关键词： game theory multifactor bounded rational charging guidance strategy particle swarm optimisation charging cost particle swarm optimisation algorithm EV users electric vehicle charging Stackelberg game model mental account theory traffic-grid coupling network power grids pricing charging price formulation method

来源：评论

学校读者我要写书评

暂无评论

Velocity obstacle-based conflict resolution and recovery method

引用

AERONAUTICAL JOURNAL 2022年第1296期126卷 345-364页

作者： Sun, F. Chen, Y. Xu, X. Mu, Y. Wang, Z. Nanjing Univ Aeronaut & Astronaut Jiangning Rd Campus Nanjing Jiangsu Peoples R China 1 Jiazi Changle East Rd Xian Shanxi Peoples R China

Considering the shortcomings of current methods for real-time resolution of two-aircraft flight conflicts, a geometric optimal conflict resolution and recovery method based on the velocity obstacle method for two aircraft and a cooperative conflict resolution method for multiple aircraft are proposed. The conflict type was determined according to the relative position and velocity of the aircraft, and a corresponding conflict mitigation strategy was selected. A resolution manoeuvre and a recovery manoeuvre were performed. On the basis of a two-aircraft conflict resolution model, a multi-aircraft cooperative conflict resolution game was constructed to identify an optimal solution for maximising group welfare. The solution and recovery method is simple and effective, and no new flight conflicts are introduced during track recovery. For multi-aircraft conflict resolution, an equilibrium point that maximises the welfare function of the group was identified, and thus, an optimal strategy for multi-aircraft conflict resolution was obtained.

关键词： collision detection and resolution cooperative game geometrical algorithm optimal solution of alliance welfare particle swarm optimisation algorithm recovery track velocity obstacle method

来源：评论

学校读者我要写书评

暂无评论

A DBN-PSOSA hybrid response surface method for bridge reliability

引用

EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING 2022年第14期26卷 7226-7239页

作者： Lu, Pengzhen Hong, Tao Xu, Zijie Wu, Ying Li, Dengguo Zhejiang Univ Technol Coll Civil Engn Hangzhou Zhejiang Peoples R China Jiaxing Nanhu Univ Jiaxing Zhejiang Peoples R China

When solving bridge reliability problems, the traditional response surface method has a highly nonlinear implicit function, which results in a low fitting accuracy and difficulty in meeting the requirements. Therefore, the dynamic Bayesian network (DBN), which is suitable for solving a multi-state unit or system uncertainty problems, is selected to construct the response surface of the implicit function. In this study, the DBN model is combined with the particle swarm optimisation based on simulated annealing (PSOSA) algorithm to improve the optimisation efficiency of model parameters and allow the constructed implicit function to simulate the structure limit state function. The DBN-PSOSA hybrid response surface analysis method is proposed for bridge failure probability calculations. A numerical example is given to demonstrate the effectiveness of the proposed method, and the reliability of an actual bridge project is analysed. The results indicate that this method has a higher calculation accuracy and efficiency compared to the traditional response surface method, and is easy to combine with the existing general finite element analysis software to achieve the rapid analysis of bridge structure reliability.

关键词： Bridge engineering reliability analysis dynamic Bayesian networks particle swarm optimisation algorithm annealing algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：