检索结果-内蒙古大学图书馆

Asia-Pacific Power and Energy Engineering Conference (APPEEC)

作者： Zhang, Yu Liu, Chongru N China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China

ISBN: (纸本)9781457705472;9781457705465

For the disadvantages of the traditional pso algorithm, which global optimum and convergence speed and accuracy are difficult to take into account together, proposed an adaptive pso algorithm using a inertia weight factor, and applied it to the PI controller parameters of HVDC optimization design to CIGRE Benchmark Model as an example, according to its structure and its electrical parameters, deduced rectifier constant current control system transfer function expression, and calculate the value of the PI parameters with adaptive particle swarm optimization, simulation results show that the proposed method is effective and practical.

关键词： HVDC pso algorithm PI controller adaptive inertia weight factor formatting

来源：评论

学校读者我要写书评

暂无评论

A New RBF Neural Network Training algorithm Based on pso

A New RBF Neural Network Training Algorithm Based on PSO

引用

2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Dingxue Zhang,Xinzhi Liu,Zhihong Guan Control science and technology Department,Huazhong University of Science & Technology Wuhan,Hubei,430074,China

Based on the study of Radial Basis Function(RBF) neural network training algorithm and Particle Swarm Optimization(pso) algorithm,a new RBF neural network training algorithm with modified pso algorithm is formulated,in which a control gene is introduced into basis pso *** algorithm can determine network structure and parameters,such as centers and widths of hidden units by combining with least square *** new training algorithm is applied to the nonlinear system identification problem,comparing with hierachical genetic algorithm and orthogonal least squares algorithm(OLS),the simulation results illustrate its efficiency.

关键词： RBF neural network pso algorithm least square method nonlinear system identification

来源：评论

学校读者我要写书评

暂无评论

Optimal placement of switching and protection devices in radial distribution networks to enhance system reliability using the AHP-pso method

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第1期27卷 181-196页

作者： Amohadi, Masoud Fotuhi-Firuzabad, Mahmud Islamic Azad Univ Dept Elect Engn Sci & Res Branch Tehran Iran Sharif Univ Technol Dept Elect Engn Tehran Iran

This paper presents a new method to determine the optimal number and locations of autorecloser and sectionalizer switches (AR/S) in distribution networks. The costs of AR/S investment, switch maintenance, and undistributed energy as well as reliability coefficients are considered in the objective function. Reliability parameters such as SAIFI, SADI, MAIFI, and ENS are evaluated in the case study system. As a new method, the weights of the reliability parameters are obtained by decision-makers using the analytical hierarchy process (AHP). The optimal size, type, and location of automatic switches are determined by minimizing the objective function using the particle swarm optimization (pso) algorithm. The effectiveness of the proposed method is tested by implementing it on two feeders of Isfahan's electric distribution network. The results indicate the efficiency and applicability of the proposed method.

关键词： AHP analysis reliability parameters objective function pso algorithm autorecloser and sectionalizer switches optimal placement

来源：评论

学校读者我要写书评

暂无评论

A new effective robust nonlinear controller based on pso for interleaved DC-DC boost converters for fuel cell voltage regulation

引用

SOFT COMPUTING 2020年第22期24卷 17051-17064页

作者： Abdelmalek, Samir Dali, Ali Bettayeb, Maamar Bakdi, Azzeddine Univ Medea Fac Technol Dept Elect Engn Medea Algeria Renewable Energy Dev Ctr CDER Algiers Algeria Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates King Abdulaziz Univ Ctr Excellence Intelligent Engn Syst CEIES Jeddah Saudi Arabia Univ Oslo Dept Math N-0851 Oslo Norway

Output voltage regulation of DC-DC converters has recently gained an increasing attention to face the many system nonidealities. The fast switching behavior is nonlinear time varying, the presence of model and measurement uncertainties, and large variations, are all inherited challenges. The aim of the present work is to design a robust nonlinear controller that ensures satisfactory and robust output voltage regulation for a proton-exchange membrane fuel cell (PEMFC) based on a DC-DC Interleaved Boost Converter (IBC). A state-space model of the DC-DC IBC is first derived using the state-space averaging technique, and a mathematical model is constructed for the PEFMC. In this regard, a robust nonlinear controller and a proportional integral controller are proposed. The controllers are tuned though particle swarm optimization algorithm to estimate their good parameters assuring the desired performance is met. The integral of absolute error criterion is used to improve the dynamic performance of the overall controlled system. Furthermore, the closed-loop stability is analyzed using the Lyapunov stability theorem, and the effectiveness of the closed-loop system is validated under various operating conditions of the PEMFC and load perturbations. Compared to other methods, the obtained results demonstrate a superior performance of the proposed control strategy in terms of its robustness to variations and uncertainties, smooth tracking of a varying set-point and faster transients.

关键词： Interleaved boost converter Proton-exchange membrane fuel cell pso algorithm Nonlinear control pso-PI controller Current ripples

来源：评论

学校读者我要写书评

暂无评论

Applied Research of ADSHpso algorithm in Multi-UAV Cooperative Mission Planning

Applied Research of ADSHPSO Algorithm in Multi-UAV Cooperati...

引用

International Conference on Mechatronics, Robotics and Automation (ICMRA 2013)

作者： Wang, Xinzeng Xiao, Jinbao Luoyang Electron Equipment Test Ctr Xinyang Peoples R China

ISBN: (纸本)9783037858066

The particle swarm optimization algorithm was improved in this paper, a novel self-adaption dynamic sub-swarms hybrid particle swarm optimization algorithm is proposed, in this algorithm, subgroup partition method based on dynamic clustering of particle adaptive value is adopted to divide particle group to different capability sub-group, then execute different optimize strategy to different subgroup, simultaneity, inertial weight and accelerating coefficient are adaptive set, through contacting adjustment of parameter with sub-group capability, the particle mode method and intersect and aberrance strategy of double deck are designed, The experimental results show that the algorithm has simple programming, good robust capability and strong optimizing capability which established the foundation of task planning of multi-UAV Cooperative.

关键词： pso algorithm Improve Strategy Unmanned Aerial Vehicle (UAV) Mission Planning

来源：评论

学校读者我要写书评

暂无评论

Import and export trade forecasting algorithm based on blockchain security and pso optimized hybrid RVM model

引用

SECURITY AND PRIVACY 2023年第2期6卷

作者： Wang, Yue Guizhou Inst Technol Coll Econ & Management Guiyang 550003 Guizhou Peoples R China

With the rapid development of globalization economy, countries in the world are more and more closely connected in business, and international import and export trade has also achieved rapid development. Based on the analysis of the status quo of the country's import and export trade, this article combines the main national economic indicators to analyze and study the algorithm of import and export trade forecasting. RVM is a sparse probability model and a new supervised learning method. Firstly, this paper introduces the protection system of block chain technology for import and export trade data information. Then, a hybrid RVM model is constructed, which is optimized by pso algorithm, and the import and export volume is predicted based on this model. Based on the analysis of the data, it is tested that the security protection coefficient of the blockchain security technology for import and export trade data can reach up to 99.9%, and the accuracy of the import and export trade forecast based on the pso optimized hybrid RVM model can reach up to 85.79%. A series of experiments show that the import and export trade forecast model studied in this paper can accurately predict the volume of import and export trade, thus providing a new method for the country's import and export trade, with high practical significance.

关键词： blockchain security hybrid RVM model import and export trade forecast pso algorithm

来源：评论

学校读者我要写书评

暂无评论

A new development of ANFIS-GMDH optimized by pso to predict pile bearing capacity based on experimental datasets

引用

ENGINEERING WITH COMPUTERS 2021年第1期37卷 685-700页

作者： Harandizadeh, Hooman Jahed Armaghani, Danial Khari, Mahdy Shahid Bahonar Univ Kerman Fac Engn Dept Civil Engn Pajoohesh SqPOB 76169133 Kerman Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Islamic Azad Univ East Tehran Branch Dept Civil Engn Tehran Iran

Prediction of ultimate pile bearing capacity with the aid of field experimental results through artificial intelligence (AI) techniques is one of the most significant and complicated problem in pile analysis and design. The aim of this research is to develop a new AI predictive models for predicting pile bearing capacity. The first predictive model was developed based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) structure optimized by particle swarm optimization (pso) algorithm called as ANFIS-GMDH-pso model;the second model introduced as fuzzy polynomial neural network type group method of data handling (FPNN-GMDH) model. A database consists of different piles property and soil characteristics, collected from literature including CPT and pile loading test results which applied for training and testing process of developed models. Also a common artificial neural network (ANN) model was applied as a reference model for comparing and verifying among hybrid developed models for prediction. The modelling results indicated that improved ANFIS-GMDH model achieved relatively higher performance compared to ANN and FPNN-GMDH models in terms of accuracy and reliability level based on standard statistical performance indices such as coefficient of correlation (R), mean square error, root mean square error and error standard deviation values.

关键词： Ultimate pile bearing capacity Deep foundation ANFIS– GMDH– pso model pso algorithm FPNN– GMDH model GMDH network

来源：评论

学校读者我要写书评

暂无评论

Enhancement neural control scheme performance using pso adaptive rate: Experimentation on a transesterification reactor

引用

JOURNAL OF VIBRATION AND CONTROL 2023年第15-16期29卷 3769-3782页

作者： Yassin, Farhat Ali, Zribi Asma, Atig Ridha, Ben Abdennour Univ Gabes Natl Engn Sch Gabes Res Lab Numer Control Ind Proc Gabes Tunisia

An arbitrary choice of the neural controller adaptive rate can have a negative effect on the performance of the closed-loop system. In this study, we propose a novel methodology for neural controller adaptive rate using Particle Swarm Optimization algorithm. The developed control scheme is composed of a recurrent neural networks emulator and controller with decoupled adaptive rates. Constraints on the adaptive rate are derived from the Lyapunov stability method. Particle Swarm Optimization is proposed as a mechanism to optimize the adaptive rate of the NC to improve the closed-loop performances. The advantages of the proposed new control algorithm are as follows: (1) online optimal choice of adaptive rate, which reduces the effort for searching an adequate neural controller adaptive rate when considering the conventional methods and (2) ensuring stability, faster convergence, disturbance rejection, and good tracking. The efficiency of the proposed pso adaptive rate is demonstrated with numerical control of SISO nonlinear system. The obtained results prove the efficiency of the proposed NC compared to those obtained with existing methods. An application of the developed approach on a semi-batch reactor is presented to validate simulations results.

关键词： Nonlinear systems adaptive neural control lyapunov stability pso algorithm chemical reactor

来源：评论

学校读者我要写书评

暂无评论

Modeling of wax deposition produced in the pipelines using pso-ANFIS approach

引用

PETROLEUM SCIENCE AND TECHNOLOGY 2017年第20期35卷 1974-1981页

作者： Chu, Zheng-Qing Sasanipour, Jafar Saeedi, Mohammadhossein Baghban, Alireza Mansoori, Hamed Anhui Xinhua Univ Dept Math & Phys Hefei 230088 Anhui Peoples R China PUT Dept Gas Engn Ahwaz Fac Petr Engn Ahvaz Iran Texas Tech Univ Dept Ind Mfg & Syst Engn Lubbock TX 79409 USA Amirkabir Univ Technol Dept Chem Engn Mahshahr Campus Marvdasht Iran Ohio Univ Chem & Biomol Engn Dept Athens OH 45701 USA

Wax deposition in petroleum industry is one of the major problems requiring accurate predictive procedures to reduce the deficiencies and effective designing of the process. An adaptive neuro fuzzy inference system (ANFIS) model is proposed to predict the wax deposition in oily systems. Parameters of the ANFIS model are optimized using the particle swarm optimization (pso) method. Results are then compared to those previously reported by Kamari et al., demonstrating better performance of the proposed ANFIS model. Statistical and graphical approaches are employed to investigate the reliability of the proposed model, illustrating the model's capability of precise estimation of the wax deposition. Coefficient of determination (R-2) and mean squared error (MSE) values of 0.994 and 0.053 are obtained for the proposed ANFIS model, revealing the reliable prediction of wax deposition by the proposed ANFIS model.

关键词： wax deposition pipelines ANFIS pso algorithm statistical analysis

来源：评论

学校读者我要写书评

暂无评论

HYDRAULIC SYSTEM FAULT DIAGNOSIS BASED ON EMD AND IMPROVED pso-ELMAN ANN

引用

TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING 2013年第4期37卷 1189-1197页

作者： Dong, Zengshou Zhang, Xiaoyu Zeng, Jianchao Taiyuan Univ Sci & Technol Dept Elect Informat Engn Taiyuan SX Peoples R China

The element parameters of engineering machinery hydraulic system are detected, the fault eigenvector is extracted, and the information is applied to neural network fault diagnosis. Experience mode decomposition (EMD) is used to extract fault characteristic vectors in this paper, combined. with the pressure, temperature and flow rate of dominant signal as neural network's inputs. In addition, the paper improves the Elman neural network learning algorithm by the pso algorithm. It can effectively increase network convergence rate and computing power. The particle swarm is used to optimize Elman neural network weights and the threshold value and then applied in the fault diagnosis system by training the network. The results show that the method increases the neural network convergence rate and reduces diagnoses error.

关键词： experience mode decomposition pso algorithm Elman neural network fault diagnosis

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：