检索结果-内蒙古大学图书馆

Improved Metaheuristic Optimization algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

引用

ENERGIES 2021年第3期14卷 619页

作者： Liu, En-Jui Hung, Yi-Hsuan Hong, Che-Wun Natl Tsing Hua Univ Dept Power Mech Engn Hsinchu 30013 Taiwan Natl Taiwan Normal Univ Undergrad Program Vehicle & Energy Engn Taipei 106 Taiwan

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 center dot 10(-4)). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

关键词： metaheuristic optimization algorithm particle swarm algorithm photovoltaic cell proton exchange membrane fuel cell

来源：评论

学校读者我要写书评

暂无评论

Network Security Posture Prediction Based on SAPSO-Elman Neural Networks

Network Security Posture Prediction Based on SAPSO-Elman Neu...

引用

International Conference on Artificial Intelligence and Computer Engineering (ICAICE)

作者： Ren, Xun-yi Luo, Qi-qi Shi, Chen Huang, Jia-ming Nanjing Univ Posts & Telecommun Coll Comp Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728191461

With the increasing popularity of the Internet, mobile Internet and the Internet of Things, the current network environment continues to become more complicated. Due to the increasing variety and severity of cybersecurity threats, traditional means of network security protection have ushered in a huge challenge. The network security posture prediction can effectively predict the network development trend in the future time based on the collected network history data, so this paper proposes an algorithm based on simulated annealing-particle swarm algorithm to optimize improved Elman neural network parameters to achieve posture prediction for network security. Taking advantage of the characteristic that the value of network security posture has periodicity, a simulated annealing algorithm is introduced along with an improved particle swarm algorithm to solve the problem that neural network training is prone to fall into a local optimal solution and achieve accurate prediction of the network security posture. Comparison of the proposed scheme with existing prediction methods validates that the scheme has a good posture prediction accuracy.

关键词： posture prediction Elman neural network particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Roughness Classification of End Milling Based on Machine Vision 3

Roughness Classification of End Milling Based on Machine Vis...

引用

3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM)

作者： Tang, Kaixuan Chen, Fumin Chang, Fan Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian Peoples R China

ISBN: (纸本)9781665441094

At present, the sample comparison method is often used in the industrial field to classify the roughness of end milling, which has some problems such as high requirements to inspectors and subjective inspection results. So this paper proposes a classification method of end milling roughness based on machine vision. Firstly, the image acquisition device combined by a mobile phone camera and a miniature microscope is used to capture surface images of the end milling sample. Secondly, the image dataset is constructed by expanding the image sample size and preprocessing image. Then the classification results of the improved LeNet-5 and AlexNet are compared to determine the more appropriate structure. Finally, particle swarm optimization (PSO) is used to optimize the model. The experimental results prove that the classification accuracy of the improved PSO-AlexNet is higher than the improved LeNet-5 and AlexNet, and can meet the roughness classification requirements. So this method can eliminate the influence of human factors and evaluate the classification results of end milling roughness objectively and accurately.

关键词： end milling roughness image classification convolutional neural network particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence Based Optimization Techniques: A Review 1st

Artificial Intelligence Based Optimization Techniques: A Rev...

引用

1st International Conference on Intelligent Computing Techniques for Smart Energy Systems (ICTSES)

作者： Swarnkar, Agrani Swarnkar, Anil Manipal Univ Jaipur Dept Comp Sci Engn Jaipur 303007 Rajasthan India Malaviya Natl Inst Technol Dept Elect Engn Jaipur Rajasthan India

ISBN: (纸本)9789811502149;9789811502132

Many artificial intelligence based optimization techniques have been introduced since the early 60s. This paper provides a brief review of some of the well-known optimization techniques, e.g., Genetic algorithm, particle swarm algorithm, and Ant Colony Optimization and recently developed techniques, e.g., BAT algorithm and Elephant Herding Optimization. All these techniques are population-based search algorithms, in which the initial population is created randomly initializing input parameters within the specified range. They approach toward the best solution inspired by the behavior of natural entities. All of these techniques have a potential to provide optimal or near-optimal solutions.

关键词： Ant colony optimization BAT algorithm Elephant herding optimization Genetic algorithm particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Battery Pack State of Health Prediction Based on the Electric Vehicle Management Platform Data

引用

WORLD ELECTRIC VEHICLE JOURNAL 2021年第4期12卷 204页

作者： Li, Xiaoyu Wang, Tengyuan Wu, Chuxin Tian, Jindong Tian, Yong Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518060 Peoples R China Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China

In electric vehicle technologies, the state of health prediction and safety assessment of battery packs are key issues to be solved. In this paper, the battery system data collected on the electric vehicle data management platform is used to model the corresponding state of health of the electric vehicle during charging and discharging processes. The increment in capacity in the same voltage range is used as the battery state of health indicator. In order to improve the modeling accuracy, the influence of ambient temperature on the capacity performance of the battery pack is considered. A temperature correction coefficient is added to the battery state of health model. Finally, a double exponential function is used to describe the process of battery health decline. Additionally, for the case where the amount of data is relatively small, model migration is also applied in the method. particle swarm optimization algorithm is used to calibrate the model parameters. Based on the migration battery pack model and parameter identification method, the proposed method can obtain accurate battery pack SOH prediction result. The method is simple and easy to perform on the electric vehicle data management platform.

关键词： electric vehicle data battery modelling state of health prediction particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Excitation performance enhancement based on optimization of structural parameters of precision-controllable vibrator

引用

ADVANCES IN MECHANICAL ENGINEERING 2021年第6期13卷

作者： Huang, Zhiqiang Wang, Ruhao Li, Gang Zhang, Kai Southwest Petr Univ Electromech Engn Coll 8 Xindu Ave Chengdu 610500 Sichuan Peoples R China

This paper focuses on solving the problem of poor excitation performance of precision- controllable vibrator leading to shallow exploration depths, which cannot fully meet the current demand for deep oil and gas resources exploration. For this purpose, the evaluation parameters of the excitation performance of the precision-controllable vibrator are clarified, an optimization model of the structure parameters of the precision-controllable vibrator is established. particle swarm algorithm and Pareto optimization logic are used to integrate the solution set to optimize the structure of precision-controllable vibrator and enhance the excitation performance of precision-controllable vibrator.

关键词： Precision-controllable vibrator excitation performance structural optimization particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Research on Intelligent Ship Path Planning Optimization Based on Chaos particle swarm Optimization

International Core Journal of Engineering

引用

International Core Journal of Engineering 2020年第12期6卷 407-416页

作者： Yuming Pan Jinbiao Chen Ziran Yuan

Under the background of the popular research field of intelligent ship path planning, this paper aims to optimize the application of particle swarm algorithm in intelligent ship path planning. Aiming at the problem of particle swarm optimization easily falling into local optimality in ship path planning, this paper introduces chaotic interference, and obtains an improved Chaotic particle swarm Optimization (Based on Chaotic particle swarm Optimization, hereinafter referred to as BCPSO). BCPSO increases the inertia weight of the particle swarm algorithm in the later stage to improve the probability of the particle swarm algorithm jumping out of the local optimum to obtain the global optimum, thereby improving the global search ability of the particle swarm algorithm in intelligent ship path planning. Through simulation experiments, compared with two similar algorithms in terms of convergence and global search ability, it is verified that the algorithm in this paper has better convergence and global optimization ability, and it has a good effect to make intelligent ship path planning and collision avoidance decision-making.

关键词： Chaos Theory particle swarm algorithm Path Planning Intelligent Ship

来源：评论

学校读者我要写书评

暂无评论

Bi-dimensional Empirical Mode Decomposition algorithm Based on particle swarm-Fractal Interpolation

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第12期12卷 5955-5977页

作者： An, Feng-Ping He, Xin Huaiyin Normal Univ Sch Phys & Elect Elect Engn Huaian 223300 JS Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 BJ Peoples R China Henan Univ Inst Intelligent Network Syst Kaifeng 475004 Peoples R China

Performance of the interpolation algorithm used in the technique of bi-dimensional empirical mode decomposition directly affects its popularization and application, so that the researchers pay more attention to the algorithm reasonable, accurate and fast. However, it has been a lack of an adaptive interpolation algorithm that is relatively satisfactory for the bi-dimensional empirical mode decomposition (BEMD) and is derived from the image characteristics. In view of this, this paper proposes an image interpolation algorithm based on the particle swarm and fractal. Its procedure includes: to analyze the given image by using the fractal brown function, to pick up the feature quantity from the image, and then to operate the adaptive image interpolation in terms of the obtained feature quantity. All parameters involved in the interpolation process are determined by using the particle swarm optimization algorithm. The presented interpolation algorithm can solve those problems of low efficiency and poor precision in the interpolation operation of bi-dimensional empirical mode decomposition and can also result in accurate and reliable bi-dimensional intrinsic modal functions with higher speed in the decomposition of the image. It lays the foundation for the further popularization and application of the bi-dimensional empirical mode decomposition algorithm.

关键词： Fractal particle swarm algorithm Bi-dimensional Empirical Mode Decomposition Optimization Image Interpolation

来源：评论

学校读者我要写书评

暂无评论

Study on the Evaluation Methodology of Landslide Susceptibility Based on Spatial-scale Analysis

引用

Journal of World Architecture 2025年第1期9卷 47-52页

作者： Zijing Lin Jian Tang Yiling Dai Bing Luo Anqi Chen School of Architectural Engineering Jiangxi Science&Technology Normal UniversityNanchang 330013JiangxiChina

Landslides are significant natural geological *** susceptibility evaluation involves the quantitative assessment and prediction of potential landslide locations and their *** has explored susceptibility assessment methods based on spatial-scale *** evaluation integrates two models—global and local scale—using a CNN model and a PSO-CNN coupled *** aspects include selecting evaluation factors and optimizing model parameters for landslide susceptibility at different scales.A major focus of current landslide research is utilizing prediction results to enhance prevention and control measures.

关键词： Landslide susceptibility evaluation Spatial-scale analysis Lixian county Geographical weighted regression particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Application of swarm Intelligence Optimization algorithm in Antenna Design

Application of Swarm Intelligence Optimization Algorithm in ...

引用

第六届智能交通、大数据及智慧城市国际会议

作者： Xiaodong Zheng School of electronic and electrical engineering Dongguan Polytechnic

Traditional intelligent algorithm cannot optimize multiple parameters at the same time,so the optimization effect is *** order to solve such problem,swarm intelligence optimization algorithm is used in the antenna *** analyzing the influence parameters,we design the objective function and adopt chaos optimization algorithm to optimize the initial population selection of particle swarm ***,according to the actual design requirements,the improved particle swarm optimization(PSO) algorithm is used to solve multiple optimization problems,and the optimal design of the antenna is also *** simulation results show that our scheme optimizes the design effect,and the antenna has larger directional gain,which effectively improves the comprehensive performance of antenna.

关键词： population intelligence optimization algorithm antenna design algorithm application particle swarm 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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：