Fluid Network Parameters Modeling Based on Particle Swarm Optimization

被引：0
|
作者：
Zhang Yue [1 ]
Men Yuhan [1 ]
Liu Yunfei [1 ]
机构：
[1] North China Elect Power Univ, Automat Dept, Beijing 071000, Peoples R China
来源：
PROCEEDINGS OF THE 35TH CHINESE CONTROL CONFERENCE 2016 | 2016年
关键词：
fluid network; signal flow graph; mechanism model; PSO; identify;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In the Power Plant Simulation System, Fluid network is widely present. Model accuracy largely determines the merits of the simulation system. The concept of signal flow graph has been introduced into the mechanism modeling process to describe the fluid network. Various structures in the fluid network are classified as node and branch, and the algorithm relationship of node's pressure and According to identify the unknown parameters in the model using the PSO, the influence from parameters of PSO to the process of optimization could be studied. Then the range of unknown parameters could be determined. Optimization could be accelerated, so as to achieve optimal results.
引用
收藏
页码：1909 / 1914
页数：6
相关论文
共 50 条
  • [41] Anchoring Parameters Optimization of Tunnel Surrounding Rock Based on Particle Swarm Optimization
    Feiyang Li
    Annan Jiang
    Shuai Zheng
    Geotechnical and Geological Engineering, 2021, 39 : 4533 - 4543
  • [42] Parameters optimization of dual clutch transmission based on hybrid particle swarm optimization
    Du C.-Q.
    Cao X.-L.
    He B.
    Ren W.-Q.
    Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2020, 50 (05): : 1556 - 1564
  • [43] Anchoring Parameters Optimization of Tunnel Surrounding Rock Based on Particle Swarm Optimization
    Li, Feiyang
    Jiang, Annan
    Zheng, Shuai
    GEOTECHNICAL AND GEOLOGICAL ENGINEERING, 2021, 39 (06) : 4533 - 4543
  • [44] The algorithms optimization of artificial neural network based on particle swarm
    Yang, Xin-Quan, 1600, Bentham Science Publishers B.V., P.O. Box 294, Bussum, 1400 AG, Netherlands (08):
  • [45] Maximizing Network Utilization for SDN Based on Particle Swarm Optimization
    Yao, Xibo
    Wang, Hua
    Gao, Chuangen
    Yi, Shanwen
    2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016), 2016, : 921 - 925
  • [46] A Road Network Matching Method Based on Particle Swarm Optimization
    Zhu, Fengxia
    Wang, Peng
    2019 THE 3RD INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPILATION, COMPUTING AND COMMUNICATIONS (HP3C 2019), 2019, : 59 - 62
  • [47] Particle swarm optimization for network-based data classification
    Carneiro, Murillo G.
    Cheng, Ran
    Zhao, Liang
    Jin, Yaochu
    NEURAL NETWORKS, 2019, 110 : 243 - 255
  • [48] A Particle Swarm Optimization-Based Generative Adversarial Network
    Song, Haojie
    Xia, Xuewen
    Tong, Lei
    INTERNATIONAL JOURNAL OF COGNITIVE INFORMATICS AND NATURAL INTELLIGENCE, 2024, 18 (01)
  • [49] The Research of Fuzzy Neural Network Based on Particle Swarm Optimization
    Man Chun-tao
    Zhang Cai-yun
    Zhang Lu-qi
    Liu Qing-yu
    PROCEEDINGS OF 2013 2ND INTERNATIONAL CONFERENCE ON MEASUREMENT, INFORMATION AND CONTROL (ICMIC 2013), VOLS 1 & 2, 2013, : 1122 - 1125
  • [50] Optimizing multiple process parameters in fused deposition modeling with particle swarm optimization
    Arup Dey
    David Hoffman
    Nita Yodo
    International Journal on Interactive Design and Manufacturing (IJIDeM), 2020, 14 : 393 - 405
12345