检索结果-内蒙古大学图书馆

2011 China Control and Decision Conference(2011中国控制与决策会议 CCDC)

作者： Liqing Xiao Huaxiang Wang Xiaoju Xu School of Electrical Engineering & Automation Tianjin UniversityNankai DistrictTianjin 300072Chin School of Electrical Engineering & Automation Tianjin UniversityNankai DistrictTianjin 300072Chin epartment of Information & Electrical Engineering Xuzhou Institute of Technology Xuzhou Jiangsu 221

In order to overcome the disadvantages that simple genetic algorithm had in low-dimensional and multimodal function optimization, an improved algorithm was proposed. The new algorithm divided the search interval into different areas by method of orthogonal decomposition, produced initial populations that had different individual distribution density by using interval algorithm, and gave each individual an attribute that marked the area which it was in and guaranteed there was more than one individual in each area during the operating process of the algorithm. particle swarm algorithm was introduced to the mutation operation of genetic algorithm to overcome the shortage of genetic al gorithm, that was, the poor search ability in local areas, especially those that had low individual distribution density. When applied to six-hump camel back function and Branin RCOS function optimization, the simulation results show that, under the same experiment condition, in contrast to other improved algorithms, the new one improves the probability of searching to all extreme points, and has stronger ability of low-dimensional multi-modal function optimization, on the premise that the convergence precision is not affected.

关键词： Genetic algorithm Interval algorithm particle swarm algorithm Six-hump Camel Back Function Branin RCOS Function

来源：评论

学校读者我要写书评

暂无评论

Prediction of Port Container Throughput Based on PSO Optimization BP Neural Network Model 22

Prediction of Port Container Throughput Based on PSO Optimiz...

引用

Proceedings of the 8th International Conference on Industrial and Business Engineering

作者： Guangying Jin Wei Feng Qingpu Meng School of Maritime Economics and Management Dalian Maritime University China

ISBN: (纸本)9781450397582

The development of the container transportation industry has made a huge contribution to the development of the regional economy. Container transportation saves cost and time and increases transportation benefits. Container throughput forecasts are very important for logistics companies, shipping companies, port authorities and shipyards. Such forecasts enable shipping companies and port operators to develop appropriate short- to medium-term strategies to remain competitive, while playing a fundamental role in allocating resources to the market. In this paper, particle swarm algorithm (PSO) is used to optimize the BP neural network model, so as to construct the PSO-BP neural network model, write the PSO-BP code and collect the historical throughput data of Shenzhen Port containers, and set the relevant parameters to use the PSO-BP model to Predicting the container throughput of Shenzhen Port, the experimental results show that the accuracy of the PSO-BP neural network model is higher than that of the unoptimized BP neural network model.

关键词： particle swarm algorithm BP Neural Network Container PSO-BP Neural Network Container Throughput

来源：评论

学校读者我要写书评

暂无评论

Research on Fractional Order Control Based on Short Memory Rule of Remote Control Weapon Station

Research on Fractional Order Control Based on Short Memory R...

引用

Electron Device and Mechanical Engineering (ICEDME), International Conference on

作者： Yuanzheng Zhao Li Zhou Pengfei Ding Zhengzhou Electromechanical Engineering Research Institute Zhengzhou China

ISBN: (数字)9781728181455

ISBN: (纸本)9781728181462

Aiming at the random disturbance in the tracking and shooting state of the remote control weapon station, stm32f407 is used as the main control unit to design the fractional order composite controller. A method of parameter self-tuning of the fractional order controller based on the improved particle swarm optimization algorithm is proposed, which focuses on the research of obtaining the digital implementation method of fractional order control algorithm. Based on the short memory rule and the time domain numerical method of calculus operator, the fractional order digital discretization is realized, and the experiment is carried out in the system debugging, and the ideal control effect is achieved.

关键词： fractional remote control weapon station fractional order stochastic disturbance REMOTE CONTROL weapons control effect STATIONS TIME-DOMAIN particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

PSO-BP neural network based on physical health perception of the elderly in retirement home

PSO-BP neural network based on physical health perception of...

引用

第35届中国控制与决策会议

作者： Jiujun Bai Ying Zhao Xuebo Chen School of Electronic and Information Engineering University of Science and Technology Liaoning China Broadcast Liaoning Network School of Management Anshan Normal University

The recreational community retirement home staff can keep abreast of the elderly's health conditions and provide personalised ***,how health status is collected and judged is more traditional,and there is a lack of prediction of the healthy development of the *** information and intelligent upgrading of the elderly recreation system is imperative,and neural network modelling based on the Internet of Things and its intelligent optimisation methods is one of the means to do *** on 5G IoT technology,this paper adopts BP neural network and particle swarm optimization modelling methods to use the physical health and long-term living environment health indicators collected by IoT terminals as model input *** selecting evaluation indicators,the construction of evaluation models,the establishment of a primary database and the determination of evaluation criteria,intelligent analysis and optimisation processes are carried out to form a health posture awareness system for the elderly in recreational community retirement home.A comparison of the simulation results shows that the proposed geriatric health situational awareness system has improved the efficiency and accuracy of its geriatric health awareness capabilities.

关键词： BP neural network particle swarm algorithm physical health perception Internet of Things artificial intelligence 5G

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Energy Management for Community Microgrids

Comparative Analysis of Energy Management for Community Micr...

引用

IEEE International Conference on Power and Energy Systems (ICPES)

作者： Md Alamgir Hossain Md Rasel Mahmud Hemanshu Roy Pota School of Engineering & Information Technology The University of New South Wales (UNSW) Canberra ACT Australia

ISBN: (数字)9781728126586

ISBN: (纸本)9781728126593

The energy management of a microgrid becomes complex due to a number of reasons, including intermittent power generation, unpredictable load demand and variable real-time electricity prices. In this paper, different energy management schemes, such as real-time and scheduling, are analysed under uncertain environments to demonstrate their effectiveness in managing a microgrid's energy. Optimisation problems are first formulated, then separate solution methods using particle swarm optimisation algorithms are applied to efficiently manage the microgrid's energy. It is observed that the scheduling program in all the case studies can reduce the operational costs significantly.

关键词： Community microgrids renewable energy optimum battery control energy management particle swarm optimisation Energy management particle swarm optimization Renewable energy scheduler program MICRO-GRID Manufacturing costs particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Comparison of the performances of Distribution State Estimation algorithms: classical Newton approach and PSO approach

Comparison of the performances of Distribution State Estimat...

引用

IEEE Power and Energy Society General Meeting

作者： O. Chilard S. Grenard O. Devaux EDF R&D (France)

ISBN: (纸本)9781467327275

Existing electricity distribution management systems (DMS) have been designed using operational and algorithmic procedures that are highly centralised. As more of the distribution network becomes active, accurately estimating the state of the system becomes essential and therefore DMS must include functions to achieve the required near to real-time state estimation. The objective of this paper is to evaluate and to compare the performances obtained with two different solutions developed by EDF R&D for the Distribution State Estimator (DSE) algorithm for MV networks: classical optimization resolution approach using a Newton resolution algorithm on one side and a particle swarm Optimization algorithm on the other side. The performances of these solutions are evaluated in terms of precisions obtained for the estimates related to the primary and secondary variables and of computation times.

关键词： Distribution State Estimation (DSE) SCADA Distribution Management Systems Sensors PSO algorithm. State estimation Newton Power grids particle swarm algorithm Precision parametric subharmonic oscillator algorithms

来源：评论

学校读者我要写书评

暂无评论

Virtual Cancelation Plume for Multiple Odor Source Localization

Virtual Cancelation Plume for Multiple Odor Source Localizat...

引用

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Goncalo Cabrita Lino Marques Veysel Gazi Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra 3030-290 Coimbra Portugal Department of Electrical and Electronics Engineering Istanbul Kemerburgaz University Mahmutbey Dilmenler Caddesi No: 26 34217 Bagcilar Istanbul Turkey

ISBN: (纸本)9781467363563

This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor source invisible to the robots so that the declared source and the odor plume it generates do not interfere with the effects of other existing plumes, allowing the localization of the remaining sources. Exploration and plume tracking by the robots is achieved using a decentralized asynchronous particle swarm optimization algorithm. The divergence operator is used to declare the odor sources. A set of simulations and real world experiments are performed on two different scenarios on a controlled environment using a swarm of 5 robots to validate the proposed methodology. Results show that the virtual plume cancelation algorithm can be successfully used to find multiple odor sources, even when two plumes overlap. It can also extend the operation of many odor source localization algorithms developed for single source localization.

关键词： Position measurement Smell Robot Odor dike swarms Controlled Environment Virtual multi-robot systems particle swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

GPU/QoE-Aware server selection using metaheuristic algorithms in multiplayer cloud gaming 18

GPU/QoE-Aware server selection using metaheuristic algorithm...

引用

Proceedings of the 16th Annual Workshop on Network and Systems Support for Games

作者： Hossein Ebrahimi Dinaki Shervin Shirmohammadi University of Ottawa Ottawa Canada

ISBN: (纸本)9781538660980

The combination of cloud computing and advancements in GPU have made many real time services possible, including Cloud Gaming (CG). Doing all the process-intensive tasks in the cloud frees players from upgrading their heterogeneous devices and installing new software, and lets them play wherever and whenever. However, higher quality is always demanded by players. For instance, as frame rate has major impact on the player's gaming performance, demand of higher frame rate is increasing. On the other hand, service providers aim to offer cost effective services. Management of the graphic-intensive CG service demand and maximizing the service providers' benefits is an issue that must be addressed properly. As remote GPU plays the main role of rendering and is the most expensive infrastructure rented by the service provider, its appropriate management is vital to address the above issue. To do so, we formulate the problem in an efficient manner and propose two methods to maximize both GPU utilization and the users' quality of experience (QoE) at the same time, subject to the constraints of the servers. Our methods are based on two metaheuristic algorithms to solve an NP-Hard optimization problem for GPU-based server selection. Our simulation results shows that by increasing the number of players, both algorithms have increasing performance in terms of GPU utilization, reduced capacity wastage, and QoE.

关键词： particle swarm algorithm cloud gaming frame rate quality of experience GPU utilization server selection genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

Relaxed Fuzzy c-Means Clustering algorithm and its application

Relaxed Fuzzy c-Means Clustering Algorithm and its applicati...

引用

2019计算智能、工程与信息技术世界大会

作者： Chuan-jun Wen Li-fan Chen Qi Zhang School of Mathematical Sciences Changzhou Institute of Technology School of Arts and Sciences Shanghai University of Medicine and Health Sciences

The FCM algorithm was sensitive to noise data due to the normalized constraint of fuzzy membership.A novel clustering algorithm is proposed and named as relaxed fuzzy C-means clustering（RFCM） in this paper,the objective function of PCM is utilized as the objective function of RFCM,and RFCM loosens the normalized constraint and only requests the whole summation of n samples' fuzzy memberships equal to n and 0≤u≤1,particle swarm optimization algorithms（PSO） are optimally used to select the fuzzy memberships of RFCM,and the value scope of fuzzy index m is extended to m>0,the iterative formula of clustering centers are derived by gradient method for *** anti-noise performance of RFCM is analyzed theoretically,and the rationality of new value scope of m>0 is explained for RFCM,and the convergence of RFCM is discussed *** effectiveness and anti-noise performance of RFCM are proved through simulation experiments on two-dimensional Gaussian data-set for anti-noise and clustering accuracy tests.

关键词： fuzzy clustering normalization constraint fuzzy index particle swarm algorithm noise data

来源：评论

学校读者我要写书评

暂无评论

Comparative study on different neural networks for network security situation prediction

引用

SECURITY AND PRIVACY 2021年第1期4卷

作者： Wang, Gang Shaanxi Police Coll 1 Qiyuan 2nd Rd Xian 710021 Shaanxi Peoples R China

This article mainly studied the performance of different neural networks in the processing network security situation prediction (NSSP). Radial basis function (RBF) and back propagation neural network (BPNN) models were optimized by particle swarm optimization (PSO) algorithm and seeker optimization algorithm (SOA), respectively. Then the PSO-RBF model and SOA-BPNN model were obtained, and comparative experiments were carried out on CNCERT/CC data set. The results suggested that the improved models were more accurate in predicting the situation value compared with RBF and BPNNmodels;the PSO-RBF mode had three prediction errors, with 0.05 mean square error (MSE) and 0.05 mean absolute error (MAE), and the SOA-BPNNmodel had six prediction errors, with 0.2 MSE and 0.13 MAE, which showed that the PSO-RBF model had better performance. The experimental results show that the PSO-RBF model has an excellent performance in processing NSSP and can be promoted and applied in practice.

关键词： comparative study network security situation prediction neural network particle swarm algorithm radial basis function

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：