检索结果-内蒙古大学图书馆

2016 International Conference on Modeling, Simulation and optimization Technologies and Applications（MSOTA 2016）

作者： Zifan Wei Songlin Yang Shengping Jing Man Liu Jiangsu Modern Shipbuilding Technology Co Ltd Jiangsu University of Science and Technology

ISBN: (纸本)9781510835702

Taking the comprehensive performance of a new type of high-speed unmanned surface vehicle(USV) including its rapidity, maneuverability, seakeeping and overturning resistance into consideration, this paper establishes a mathematical model of the performance comprehensive optimization of high-speed USV. And according to the mathematical model and optimization algorithm, comprehensive optimization software based on a variety of optimization algorithms was designed and compiled to analyze the relevant calculation. The results indicates that the optimization effect of the growth mechanism is more superior to the roulette mechanism;genetic algorithm of growth mechanism and particle swarm optimization algorithm would have the best results in the optimization of the 5000 th generation, and it can get the rapidity optimization system had greater influence on the total optimization system;and what's more, the optimal values of hull length and other key design variables and partial parameters of different algorithms are obtained.

关键词： high-speed USV comprehensive optimization genetic algorithm particle swarm optimization algorithm key variables

来源：评论

学校读者我要写书评

暂无评论

Optimal Configuration of Micro-grids Power Supply Based on Operating Modes

Optimal Configuration of Micro-grids Power Supply Based on O...

引用

2016 IEEE International Conference on Power and Renewable Energy （ICPRE 2016）

作者： Lei Bin Yu Xueying Li Huaqiang Sichuan Electric Power Corporation Power Economic Research Institute Sichuan University

The reasonable capacity configuration of distributed generation in a micro-grid is the prerequisite of its reliable and economical operation. This paper establishes a optimal configuration model of micro grid power supply aiming to reach the goal of the optimal comprehensive benefits of micro grid, and then solve the configuration model by using particle swarm optimization algorithm.

关键词： micro-grid comprehensive benefits power supply optimal configuration particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

The study of PSO-RBF neural network generalized predictive control strategy in unit plant

The study of PSO-RBF neural network generalized predictive c...

引用

2016 4th International Conference on Advanced Materials and Information Technology Processing(AMITP 2016)

作者： WANG HUI LING Hujun PAN LEI school of electric power inner mongolia university of technology inner mongolia university of technology

Unit coordinated control in thermal power plants is a system which is complex,nonlinear and is difficulty to establish accurate model, So it is hard to make system gain optimum running effect with conventional control strategy. PSO-RBF neural network is used to identify the mathematical model of coordinated control system and acts as a predictive model in generalized predictive control strategy, which is to achieves predictive control with online rolling optimization and real time feedback revision. Simulation results show that it has a strong robustness when the load condition changes,or big lag affects.

关键词： particle swarm optimization algorithm RBF neural network generalized predictive control generating unit

来源：评论

学校读者我要写书评

暂无评论

An Improved PSO-BP Neural Network and Its Application to Earthquake Prediction

An Improved PSO-BP Neural Network and Its Application to Ear...

引用

第28届中国控制与决策会议

作者： Cao Li Xiaoyu Liu School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (纸本)9781467397155

This paper presents a way of combining BP(Back Propagation) neural network and an improved PSO(particle swarm optimization) algorithm to predict the earthquake *** is known that the BP neural network and the normal PSO-BP neural network have some defeats,such as the slow convergence rate,easily falling into local minimum *** improving the properties of PSO,some proposed the linear decreasing inertia weight ***,this paper uses a nonlinear decreasing inertia weight in PSO to get a faster training speed and better optimal *** with the linear decreasing strategy,the inertia weight in our nonlinear method has a faster declining speed in the early iteration,which can enhance the searching *** the late iteration,the inertia weight has a slower declining speed to avoid trapping in local minimum *** we apply the improved PSO to optimize the parameters of BP neural *** the end,the improved PSO-BP neural network is applied to earthquake *** simulation results show that the proposed improved PSO-BP neural network has faster convergence rate and better predictive effect than the BP neural network and the normal PSO-BP neural network.

关键词： BP neural network particle swarm optimization algorithm Inertia Weight Earthquake Prediction

来源：评论

学校读者我要写书评

暂无评论

Phase Dependent and Independent Frequency Identification of Weak Signals Based on Duffing Oscillator via particle swarm optimization

引用

CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2014年第1期33卷 223-239页

作者： Chang, Yuan Hao, Yi Li, Chunwen Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Datang Microelect Technol Co Ltd Beijing 100094 Peoples R China

In detecting weak signals based on the Duffing oscillator, it is usually assumed that the frequency is known, which is not always the case. This paper studies the problem of detecting the frequency of the to-be-detected weak signal based on the Duffing oscillator. For this purpose, the variance of the Duffing oscillator's output is exploited, which has the property of multi-extremum single-maximum (MESM) distribution with the frequency of the periodic signal. The impact of signal's phase on the MESM distribution is discussed. When the signal's phase is known, the frequency of the signal can be directly identified as that with the maximal variance, which leads to a nonlinear optimization problem that can be solved by a particle swarm optimization (PSO) algorithm. When the phase is unknown, the pi/2-phase-shift method is to be exploited integrated with a PSO algorithm. It is shown that the frequency can be precisely and efficiently identified by this method, whose effectiveness is verified by simulation results in Matlab.

关键词： Weak signal detection Duffing Chaotic Oscillator Frequency detection Variance particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A particle swarm optimization-based algorithm for finding gapped motifs

引用

BIODATA MINING 2010年第1期3卷 9-9页

作者： Lei, Chengwei Ruan, Jianhua Univ Texas San Antonio Dept Comp Sci San Antonio TX 78249 USA

Background: Identifying approximately repeated patterns, or motifs, in DNA sequences from a set of co-regulated genes is an important step towards deciphering the complex gene regulatory networks and understanding gene functions. Results: In this work, we develop a novel motif finding algorithm (PSO+) using a population-based stochastic optimization technique called particle swarm optimization (PSO), which has been shown to be effective in optimizing difficult multidimensional problems in continuous domains. We propose a modification of the standard PSO algorithm to handle discrete values, such as characters in DNA sequences. The algorithm provides several features. First, we use both consensus and position-specific weight matrix representations in our algorithm, taking advantage of the efficiency of the former and the accuracy of the latter. Furthermore, many real motifs contain gaps, but the existing methods usually ignore them or assume a user know their exact locations and lengths, which is usually impractical for real applications. In comparison, our method models gaps explicitly, and provides an easy solution to find gapped motifs without any detailed knowledge of gaps. Our method allows the presence of input sequences containing zero or multiple binding sites. Conclusion: Experimental results on synthetic challenge problems as well as real biological sequences show that our method is both more efficient and more accurate than several existing algorithms, especially when gaps are present in the motifs.

关键词： particle swarm optimization particle swarm optimization algorithm Motif Finding Motif Length Real Motif

来源：评论

学校读者我要写书评

暂无评论

Model of coal product structure based on particle swarm optimization algorithm

引用

Procedia Earth and Planetary Science 2009年第1期1卷 640-647页

作者： Zhang-guo, Wang Ya-li, Kuang Zhe, Lin Chang-sheng, Shi School of Chemical Engineering and Technology China University of Mining and Technology Xuzhou 221116 China Environmental Engineering Department North China Institute of Science and Technology Beijing 101601 China

Planning rational product structure of coal preparation is the key to attain the maximization of economic benefit in coal preparation enterprise and to save energy resources. There are many factors effect the preparation product structure, such as raw coal quality, separating methods, coal price, processing cost, product quality demands and equipment performance, etc. The research focuses on the optimization of product structure under the Multi-factor influences. In order to maximizing the economic benefit, the algorithm model of product structure is established, and the multiple influence factors are transformed as model parameters and constraint conditions. Then the particle swarm optimization (PSO) algorithm is used to search the optimal scheme of product structure. According to the actual requirement, the model was divided into several child models during the calculation. A set of practical software has been developed based on the research. The result shows that using PSO algorithm can get better convergence effect and avoid the local optimization for the Multi-factor model and that the optimal scheme of product structure from the model accord with the practical situation. © 2009.

关键词： particle swarm optimization (PSO) algorithms Coal deposits Coal industry Coal preparation Coal research Convergence of numerical methods Energy resources Industrial research Shape optimization Structural optimization algorithm model Child models Coal products Constraint conditions Economic benefits Equipment performance Gravity separation Influence factors Local optimizations Model optimization Model parameters Multi factor Optimal scheme particle swarm optimization algorithm Processing costs Product quality Product structure PSO algorithms Raw coal Save energy

来源：评论

学校读者我要写书评

暂无评论

Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

引用

APPLIED ACOUSTICS 2014年第0期80卷 57-67页

作者： Hajihassani, M. Armaghani, D. Jahed Sohaei, H. Mohamad, E. Tonnizam Marto, A. Univ Teknol Malaysia Dept Geotech & Transportat Fac Civil Engn Utm Skudai 81310 Johor Malaysia

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Quarry blasting Airblast-overpressure Artificial neural networks particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Robust H-infinity Tracking Control Combined with Optimized PSS by PSO algorithm for Multimachine Power System

引用

JOURNAL OF ELECTRICAL SYSTEMS 2015年第1期11卷 36-48页

作者： Dib, Faiza Ben Meziane, Khaddouj Boumhidi, Ismail Sidi Mohamed Ben Abdellah Univ Fac Sci Dept Phys LESSI Lab Dhar Elmahrez Fez Morocco

In this paper, the proposed controller design is based on H-infinity tracking control combined with the optimized Power System Stabilizer (PSS). In addition the parameters of the PSS controller are optimized using the particle swarm optimization algorithm (PSO). The aim of this study is to obtain a high performance for the speed deviation and the angle rotor simultaneously, also the damping of the oscillations and the enhancing power system stability. Using the H-infinity tracking control show the convergence of the errors to the neighborhood of zero. In order to test the effectiveness of the proposed method, the simulation results clearly indicate the damping of the oscillations of the angle rotor and angular speed with reduced overshoots which confirms the performance of the proposed scheme.

关键词： H-infinity tracking control power system stabilizer particle swarm optimization algorithm multi machine power system

来源：评论

学校读者我要写书评

暂无评论

The Study on optimization Design of Propeller Pitch

引用

JOURNAL OF COASTAL RESEARCH 2015年第Spe73期000卷 466-470页

作者： Wang, Chao Ye, Liyu Chang, Xin Huang, Sheng Harbin Engn Univ Coll Shipbldg Engn Harbin 150001 Peoples R China

With the increasing requirements of the hydrodynamic performance of the propeller, the optimization design of propeller has been gradually taken by people. With the DTRC series propellers as master model, this paper uses the theoretical prediction program based on surface panel method and combines with particle swarm optimization algorithm to study the optimization of propeller pitch (the other parameter is the same as the original propeller). In the optimization process, there are two different kinds of pitch expression (linear superposition method and Bezier function method) to fit radial distribution of pitch. With open water efficiency as the goal, the propeller is optimized and then discusses the influence of skew on open water efficiency. The result shows that Bezier method fits pitch curve more smoothly compared with hick-henne method, and that under the condition of meeting the thrust coefficient, the optimized propeller of Bezier method has higher open water efficiency.

关键词： Propeller hydrodynamic performance propeller pitch particle swarm optimization 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：