检索结果-内蒙古大学图书馆

作者： Zifa Liu Dong Peng Xinyue Wang Hui Li Hongyang Liu North China Electric Power University State Grid Economic and Technological Research Institute CO.LTD

For the multi-criteria decision-making problem of portfolio investment in power grid construction projects,the modern portfolio theory is used to construct a portfolio optimization model based on adaptive particle swarm optimization algorithm under the constraints of power demand,grid enterprise investment capability and reliability level in this *** the shortcomings of slow convergence and long operation time of particle swarm optimization,the change of inertia weight is used to change the update mode of particle position,which improves the convergence speed of the *** portfolio optimization model provides an applicable decision-making method for complex grid optimization investment decisions,and builds a complex grid optimization investment decision management process based on power demand and investment capability.

关键词： optimization model power grid project investment particle swarm optimization algorithm

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Application and Comparison of Several Intelligent algorithms on Muskingum Routing Model

Application and Comparison of Several Intelligent Algorithms...

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2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： YANG Zhengxiang Digital Engineering and Simulation Research Center,Huazhong University of Science and Technology,Wuhan Hubei,430074,China,KANG Ling School of Hydropower and Information Engineering,Huazhong University of Science and Technology,Wuhan Hubei,430074,China,

In order to solve the problem of linearization,complexity and poor accuracy for parameter estimate of Muskingum Routing Model at present,this paper introduces three modern intelligent algorithms-Genetic algorithm(GA),Simulated Annealing algorithm(SA) and particle swarm optimization algorithm(PSO) for the parameter calibration of Muskingum *** specific simulation,the results of five methods are *** according to the calculation,comparison and analysis of five methods comprehensively,it is found that the results of three modern intelligent algorithms are fit significantly and better than traditional methods.

关键词： Muskingum Model Genetic algorithm Simulated Annealing algorithm particle swarm optimization algorithm

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A New algorithm for Solving Ill-conditioned Linear System with an Auto-Parameter

A New Algorithm for Solving Ill-conditioned Linear System wi...

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2nd International Conference on Computer Engineering,Information Science & Application Technology(ICCIA 2017)

作者： Tianyu He Jinshan Wang Zonghao Tian Mathematics section Army Officer Academy

ISBN: (纸本)9781510845008

A new simple and effective method for solving ill-conditioned linear systems is presented in this paper. This method tried mainly not to decrease the error caused in direct solving, instead, it tries to transfer this error to a medium variable. At the same time, a parameter is introduced. In order to obtain the best parameter, PSO is used. An algorithm based on this method is presented, and examples show that this algorithm could solve extremely ill-conditioned linear systems correctly and stably.

关键词： particle swarm optimization algorithm auto-parameter ill-conditioned linear system

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Feature Selection for Cancer Classification Based on SRPSO algorithm

Feature Selection for Cancer Classification Based on SRPSO A...

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2019 International Conference on Informatics, Control and Robotics 2019信息学、控制和机器人学国际会议(ICICR2019)

作者： Qiu-lan XIAO Hong ZHENG Qing-an YAO School of Computer Science and Engineering Changchun University of Technology

Improving the accuracy of cancer classification plays an important role in cancer-assisted diagnosis. Genes selection is an important factor for improving the accuracy of cancer classification. In this paper, based on the standard particle swarm optimization algorithm, an SRPSO algorithm with self-adaptive and reverse-learning mechanism is proposed. It is applied to select feature genes from microarray datasets, and the results are used for cancer classification via SVM to make 5-fold cross-validation. To evaluate the performance of SRPSO, four different cancer datasets including Colon, ALLML, MLL, and SRBCT were selected. Based on the evaluation process, the SRPSO algorithm provided better results on each dataset.

关键词： Microarray gene data Cancer classification particle swarm optimization algorithm Self-adaptive Reverse-learning

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Fault Diagnosis and Reliable Configuration of Uncertain Strip Area Based on MPSO-SVM

Fault Diagnosis and Reliable Configuration of Uncertain Stri...

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第40届中国控制会议

作者： Mochen Li Bo Yao Fuzhong Wang College of Mathematics and System Science Shenyang Normal University Department of Basic Education Shenyang Institute of Engineering

For a class of linear uncertain flight control systems,in terms of the strip region,the problem of actuator continuous gain fault and reliable control is *** the same time,in order to solve the difficulty of selecting parameters of support vector machine,a parameter optimization algorithm based on improved particle swarm optimization(MPSO) is *** with grid search method,particle swarm optimization and genetic algorithm,this method has the advantages of fast convergence,short time consuming and high *** the problem that the pole information is difficult to obtain,the algorithm theory of pole observer is given to realize the real-time observation of unknown ***,the effectiveness of reliable controller design is further proved by numerical simulation.

关键词： reliable control support vector machine pole observer particle swarm optimization algorithm grid search method genetic algorithm

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Reverse engineering module networks by PSO-RNN hybrid modeling

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BMC GENOMICS 2009年第Sup1期10卷 S15-S15页

作者： Zhang, Yuji Xuan, Jianhua de los Reyes, Benildo G. Clarke, Robert Ressom, Habtom W. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC USA Univ Maine Sch Biol & Ecol Orono ME 04469 USA

Background: Inferring a gene regulatory network (GRN) from high throughput biological data is often an under-determined problem and is a challenging task due to the following reasons: (1) thousands of genes are involved in one living cell;(2) complex dynamic and nonlinear relationships exist among genes;(3) a substantial amount of noise is involved in the data, and (4) the typical small sample size is very small compared to the number of genes. We hypothesize we can enhance our understanding of gene interactions in important biological processes (differentiation, cell cycle, and development, etc) and improve the inference accuracy of a GRN by (1) incorporating prior biological knowledge into the inference scheme, (2) integrating multiple biological data sources, and (3) decomposing the inference problem into smaller network modules. Results: This study presents a novel GRN inference method by integrating gene expression data and gene functional category information. The inference is based on module network model that consists of two parts: the module selection part and the network inference part. The former determines the optimal modules through fuzzy c-mean (FCM) clustering and by incorporating gene functional category information, while the latter uses a hybrid of particle swarm optimization and recurrent neural network (PSO-RNN) methods to infer the underlying network between modules. Our method is tested on real data from two studies: the development of rat central nervous system (CNS) and the yeast cell cycle process. The results are evaluated by comparing them to previously published results and gene ontology annotation information. Conclusion: The reverse engineering of GRNs in time course gene expression data is a major obstacle in system biology due to the limited number of time points. Our experiments demonstrate that the proposed method can address this challenge by: (1) preprocessing gene expression data (e. g. normalization and missing value imputation)

关键词： particle swarm optimization Gene Expression Data Gene Regulatory Network particle swarm optimization algorithm Relative Entropy

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Research on Multi-objective Adaptive Cruise Control Method

Research on Multi-objective Adaptive Cruise Control Method

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2020中国汽车工程学会年会暨展览会

作者： Yang Lei Mao Jin Liu Kai Du Jinfu School of Mechanical and Precision Instrument Engineering Xi'an University of Technology

In order to improve ACC's ability to coordinate various targets during the following process,a multi-target adaptive cruise control algorithm was *** a longitudinal kinematics model of the workshop and introduce a variable spacing strategy. Design objective functions and constraints,which comprehensively consider factors such as safety,comfort,fuel economy,and vehicle limitations,And introduce relaxation factor vectors to soften different hard constraint boundaries to solve the problem of no feasible solution,and introduce reference trajectory. Based on the model predictive control theory,the problem is transformed into a multiconstrained quadratic programming problem,which is solved using an improved particle swarm optimization *** simulation results show that the multi-objective adaptive cruise control algorithm based on particle swarm optimization algorithm can greatly improve the vehicle's driving comfort and fuel economy.

关键词： multi-target adaptive cruise control spacing strategy model predictive control particle swarm optimization algorithm

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

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

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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

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Transmission planning with renewable generation and energy storage

Transmission planning with renewable generation and energy s...

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10th International Conference on Advances in Power System Control, Operation & Management (APSCOM 2015)

作者： Jing Zheng Fushuan Wen Minglei Zhou Liexiang Hu Qian Xu Zhou Lan State Grid Zhejiang Electric Power Company Hangzhou China Department of Electrical and Electronic Engineering Institut Teknologi Brunei Brunei

ISBN: (纸本)9781510830059

An energy storage system (ESS) can provide regulation and reserve capacities for the power system, and hence alleviate the negative impacts of renewable energy such as wind power. Nonetheless, challenges remain as to how the operation of wind farms and ESSs can be coordinated efficiently, and how transmission system expansion planning will be impacted. This paper addresses these challenges by: (1) examining the correlation between the cost of ESS and its cycle life, exampled with a Natrium Sulfur (NaS) ESS; (2) investigating the integrated operation of a wind farm and an ESS taking into account both the cost of ESSs and their smoothing effect of wind power fluctuations; (3) developing a transmission system expansion planning model with particular reference to both the investment cost of the transmission system concerned and the operating costs of wind farms and ESSs; and (4) solving the developed planning formulation using an improved particle swarm optimization algorithm. The proposed planning method is illustrated through its applications to two sample systems.

关键词： particle swarm optimization algorithm wind farm energy storage system integrated operation transmission system expansion planning

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Integrated Design of Missile Guidance and Control:Active Disturbance Rejection Control Method

Integrated Design of Missile Guidance and Control:Active Dis...

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2014中国制导、导航与控制学术会议

作者： Maopeng Ran Yifen Chen Qing Wang Chaoyang Dong Aojia Ma School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191 China The 8th Institute of Shanghai Academy of Spaceflight TechnologyShanghai201109 China School of Aeronautic Science and EngineeringBeihang UniversityBeijing100191 China

ISBN: (纸本)9781479946983

In this paper,we present an active disturbance rejection control (ADRC) based integrated guidance and control (IGC) *** IGC law is designed using the backstepping technique,and the particle swarm optimization (PSO) algorithm is utilized to find the optimal selection of the controller *** results show that,with the application of the proposed IGC law,small miss distance can be achieved,and the system is robust against target maneuver and system uncertainties.

关键词： active disturbance rejection control control nonlinearities missile guidance particle swarm optimisation uncertain systems ADRC IGC law design PSO algorithm active disturbance rejection control method backstepping technique controller parameter selection integrated design integrated guidance and control missile control particle swarm optimization algorithm system uncertainties Aerodynamics Backstepping Educational institutions History Missiles Observers Uncertainty guided missile guidance Robust control overall design system uncertainties missile control Control nonlinearities missiles particle swarm optimization Backstepping Educational institutions uncertain systems Aerodynamics Observers

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