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Application of Improved pso algorithm in Hydraulic Pressing System Identification

Journal of Iron and Steel Research International 2012年第9期19卷 29-35页

作者： YU Yu-zhen 1 , 2 , REN Xin-yi 2 , DU Feng-shan 2 , SHI Jun-jie 2 ( 1.Institute of Mechanical Engineering , Hebei United University , Tangshan 063009 , Hebei , China 2.Institute of Mechanical Engineering , Yanshan University , Qinhuangdao 066004 , Hebei , China ) Institute of Mechanical Engineering Hebei United University Tangshan Hebei China Institute of Mechanical Engineering Yanshan University Qinhuangdao Hebei China

In view of characteristics of particle swarm optimization ( pso ) algorithm of fast convergence but easily falling into local optimum value , a novel improved particle swarm optimization algorithm is put forward , and it is applicable to identify parameters of hydraulic pressure system model in strip rolling *** order to maintain population diversity and enhance global optimization capability , the algorithm is firstly improved by means of decreasing its inertia weight linearly from the maximum to the minimum and then combined with chaotic characteristics of ergodicity , randomness and sensitivity to initial *** the improved algorithm is used to identify parameters of hydraulic pressure system , the comparison of simulation curves and measured curves indicates that the identification results are reliable and close to actual situation.A new method was provided for hydraulic AGC system model identification.

关键词： hydraulic pressing system pso algorithm chaos self-adaptive parameter identification

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Mid-Term Load Power Forecasting Considering Environment Emission using A Hybrid Intelligent Approach 5

Mid-Term Load Power Forecasting Considering Environment Emis...

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5th International Symposium on Environment-Friendly Energies and Applications (EFEA)

作者： Heydari, Azim Garcia, Davide Astiaso Keynia, Farshid De Santoli, Livio Sapienza Univ Dept Astronaut Elect & Energet Engn DIAEE Via Eudossiana 18 I-00184 Rome Italy Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

ISBN: (纸本)9781538655177

The forecasting of electricity load is considered as an essential instrument, especially in countries with a restructured electricity market. The mid-term prediction is performed for the period within 1 month to 1 or 2 years and it is important for mid-term planning, including planning of repairs and economic exploitation of power systems, which are related to the reliability of the system directly. The forecast horizon in this paper is monthly and on a daily basis (peak load). The combined method of the neural network and the particle optimization algorithm were used to predict the load, and then the maximum amount of environmental pollution caused by the production of electricity required to supply the predicted load was calculated. The applied method was tested on the data of a North American electric company for four months (four seasons) and in comparison to the other methods presented in previous studies, it had an acceptable accuracy.

关键词： environment emission daily peak load neural network pso algorithm Mid-Term forecasting

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Traffic Signal Optimization Based on System Equilibrium and Bi-level Multi-objective Programming Model 7th

Traffic Signal Optimization Based on System Equilibrium and ...

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7th International Conference on Green Intelligent Transportation System and Safety (GITSS)

作者： Wang, Xiao-ting Chang, Yu-lin Zhang, Peng Jiangsu Univ Sch Automot & Traff Engn Zhenjiang 212013 Peoples R China Southeast Univ Jiangsu Key Lab Urban ITS Nanjing 211189 Jiangsu Peoples R China

ISBN: (纸本)9789811035517;9789811035500

Based on the design principle of minimizing the automotive exhaust emissions and the total impedance of the road network, an urban road traffic signal control method of bi-level multi-objective programming model is established by designing the heuristic particle swarm optimization (pso) algorithm. First of all, the upper-level model which combined the vehicle emissions model and system optimum assignment model is built, and the lower-level model is built based on minimizing the sum of the link travel time function integral. Then, the heuristic pso algorithm is designed and transformed to solve upper-level and lower-levels model iteratively by two pso algorithms. Ultimately, by altering the weight parameters of the upper model, the model is dealt with separately in case of single target and multi-target, the optimization results of which is compared with the VISSIM simulation results and the optimization results by means of heuristic genetic algorithm. The simulation results show that bi-level multi-objective control method, which could improve the operating quality of road network, is of great optimization ability and can effectively reduce the automotive exhaust emissions and the total impedance of the road network.

关键词： Traffic engineering Bi-level multi-objective programming model pso algorithm Signal control System equilibrium

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Research on Temperature Control System of Plant Factory Based on Particle Swarm Optimization

Research on Temperature Control System of Plant Factory Base...

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作者： Shigang Cui Jiejie Chen Xingli Wu Lin He Yongli Zhang Tianjin University of Technology and Education Tianjin Key Laboratory of Information Sensing & Intelligent Control

In order to solve the problems of complex temperature control and inaccurate regulation in plant factories,this paper starts from the actual model of plant factory,uses STM32 processor with embedded Cortex-M3 as the core,and collects the plant factory through Modbus fieldbus *** information,using the MATLAB tool to derive the temperature control model of the plant growth ***,the fuzzy control algorithm and particle swarm optimization(pso) optimization algorithm are used to control the temperature control model with two different *** establishing the MATLAB simulation model,the optimal control method under the plant growth cabinet model is *** results show that compared with fuzzy PID,PID control based on pso algorithm has good stability and regulation,and plants based on this environment can get better growth.

关键词： plant growth cabinet temperature control modbus fieldbus pso algorithm fuzzy PID control

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Research on capacity optimization of PV-wind-diesel-battery hybrid generation system 8

Research on capacity optimization of PV-wind-diesel-battery ...

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8th International Power Electronics Conference (IPEC-Niigata) / Energy Conversion Congress and Exposition (ECCE Asia)

作者： Zhu, Cailing Liu, Furong Hu, Sheng Liu, Shu Wuhan Univ Technol Coll Automat Wuhan Hubei Peoples R China SINOMACH Intelligence Technol Res Inst Wuhan Hubei Peoples R China

ISBN: (纸本)9784886864055

For small scale isolated PV-wind-diesel-battery hybrid generation system, the capacity optimization needs to determine capacity of each component. This paper proposes an optimization method that takes economic and technical indexes into account, which optimize the capacity of the battery and diesel generators under the premise of satisfying the technical indexes, and also optimize capacity of wind turbine (WT) and photovoltaic array (PV) by considering the economic index. Among them, the economic index of the system is to minimize the life-cycle cost (LCC). According to the wind-solar resources of the installed site, the mathematical model of the system is built after analyzing the output characteristics of each micro-source and the energy dispatching strategy. Finally, the particle swarm optimization (pso) algorithm is adopted to obtain the minimum cost and the optimal capacity configuration scheme by MATLAB simulation software. The influence of capacity of WT and PV on LCC is analyzed too.

关键词： PV-wind-diesel-battery system Economic and technical indexes Capacity optimization pso algorithm

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Tweet Summarization: A New Approch 2

Tweet Summarization: A New Approch

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Naik, Siddhi Lade, Shruti Mamidipelli, Swati Save, Ashwini Viva Inst Technol Comp Engn Mumbai India Viva Inst Technol Dept Comp Engn Mumbai India

ISBN: (纸本)9781538619742

Social Media has become a crucial source for gaining information about current goings-on in the world. Twitter is considered as fastest and most popular means of communication today where thousands of people tweet everyday regarding an event or a news-story. Number of tweets are posted on twitter on daily basis, which are analyzed by sentiment analysis to draw summary of opinions expressed by the user and categorize them to know the views of the user on an issue. The problem with sentiment analysis is that there are millions of users with differences in opinion to test. There are as well practical difficulties that arise while performing sentiment analysis. It may happen that someone may tweet some irrelevant stuff about an event and in such cases summarization comes into picture and plays a significant role. For providing user with better results tweets are not only summarized but they are first segmented. Segmentation helps in conserving semantic meaning of tweet. For summarization of tweets there are various clustering algorithms available which different researchers have used in their systems like K-means, Ant Colony. One of the optimal algorithms is the Particle Swarm Optimization algorithm (pso). pso works in an analogous way as that of swarm of bees. And hence to provide user with better results the proposed system makes use of the pso algorithm for clustering of tweets for summarization.

关键词： twitter tweets summarization pso algorithm

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Improved State Space Model Using Iterative pso for Unsteady Aerodynamic System at High AOA

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INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE 2018年第3期12卷 1-17页

作者： Luo, Guiming Zhao, Boxu Jiang, Mengqi Tsinghua Univ Sch Software Beijing Peoples R China

Due to the complex hysteresis phenomenon at a high angle of attack (AOA), modeling of unsteady aerodynamic coefficients usually encounters the problem that the parameter vector is too long and the simulation accuracy is not high. The article proposes an improved state-space model based on aerodynamics, applying Fourier analysis and the principal component analysis for model optimization. The likelihood criterion and GOIpso (Iterative Particle Swarm Optimization Based on Genetic Operator) algorithm are established under the Gaussian assumption. The iterative pso, into which the genetic algorithm's operators are integrated to calculate the optimization of the likelihood function, greatly reduced the probability of local optimization. Experiments show that the algorithm and model proposed in this paper greatly improves the model-fitting accuracy.

关键词： High Angle of Attack pso algorithm State Space Model Unsteady Aerodynamic System

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Electronic Transaction Fraud Detection Based on Improved pso algorithm

Electronic Transaction Fraud Detection Based on Improved PSO...

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2nd International Conference on Computer Science and Network Technology (ICCSNT)

作者： Shen Jia-jie E China Normal Univ Comp Ctr Sch Informat Sci & Technol Shanghai Peoples R China

ISBN: (纸本)9781467329644;9781467329637

Aiming to the problem of how to find the cheating samples of the Electronic transaction, using method of assigning the value on every particle base on the environment, the improved particle swarm optimization (pso) algorithm is presented to handle this problem. Though the theoretical derivation, the cheating sample is detected in optimization cost and the adaptability and correctness of the algorithm is proofed. The correctness of theoretical derivation is also verified by the experiment.

关键词： electronic transaction pso algorithm fraud detection

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Tolerance Optimization Design based on Physical Programming Methods and pso algorithm

Tolerance Optimization Design based on Physical Programming ...

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International Conference on Sustainable Construction Materials and Computer Engineering (ICSCMCE 2011)

作者： Qiu, H. B. Dong, Y. Y. Wang, Y. Gao, L. Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

ISBN: (纸本)9783037852583

Product tolerance is one of the key factors which can determine the good or bad performance of mechanical products. Its size not only affects the manufacturing and assembly process, but also affects product features Ell. Thus tolerance optimization design gets more and more attention. In this paper, an improved physical programming method is used to make mathematical modeling for tolerance allocation problem of assembly dimensional chain, and pso algorithm is also used to improve solving ability. And the effective solution for tolerance optimization is designed.

关键词： Tolerance optimization Tolerance allocation Physical programming Assembly dimension chain pso algorithm

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DESIGNING PID CONTROLLER FOR DC MOTOR SYSTEM BY MEANS OF ENHANCED pso algorithm WITH DISCRETE CHAOTIC LOZI MAP

DESIGNING PID CONTROLLER FOR DC MOTOR SYSTEM BY MEANS OF ENH...

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26th European Conference on Modelling and Simulation (ECMS 2012)

作者： Pluhacek, Michal Senkerik, Roman Davendra, Donald Zelinka, Ivan Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic VB TUO Dept Comp Sci Fac Elect Engn & Comp Sci Ostrava 70833 Czech Republic

ISBN: (纸本)9780956494443

The main aim of this paper is the utilization of discrete chaotic Lozi map based chaos number generator to enhance the performance of pso algorithm. This paper presents the results of research, in whichchaos enhanced pso algorithm is used to design an optimal PID controller for DC motor system. Obtained results are compared with other non-heuristic and heuristic methods.

关键词： PID controller Optimization Evolutionary algorithms pso algorithm Chaos Lozi map

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