Chinese Word Segmentation based on the Improved Particle Swarm Optimization Neural Networks

被引：0
|
作者：
He, Jia [1 ]
Chen, Lin [2 ]
机构：
[1] UESTC, Sch Engn & Comp Sci, Computat Intelligence Lab, Chengdu, Peoples R China
[2] Chengdu Univ Informat Technol, Dept Comp, Chengdu, Peoples R China
来源：
2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2008年
关键词：
Particle Swarm Optimization(PSO); Fuzzy cluster Particle Swarm Optimization(FPSO); Chinese word segmentation; BP neural networks;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Chinese word segmentation based on the improved Particle Swarm Optimization (PSO) neural networks is discussed in this paper. Firstly, a solution is obtained by searching globally using FPSO (Fuzzy cluster Particle Swarm Optimization) algorithm, which has strong parallel searching ability, encoding real number, and optimizing the training weights, thresholds, and structure of neural networks. Then based on the optimization results obtained from FPSO algorithm, the optimization solution is continuously searched by the following BP algorithm, which has strong local searching ability, until it is discovered finally. Simulation results show that the method proposed in this paper greatly increases both the efficiency and the accuracy of Chinese word segmentation.
引用
收藏
页码：803 / +
页数：3
相关论文
共 50 条
  • [21] Video deepfake detection using Particle Swarm Optimization improved deep neural networks
    Leandro Cunha
    Li Zhang
    Bilal Sowan
    Chee Peng Lim
    Yinghui Kong
    Neural Computing and Applications, 2024, 36 : 8417 - 8453
  • [22] An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks
    Han, Fei
    Ling, Qing-Hua
    Huang, De-Shuang
    NEURAL COMPUTING & APPLICATIONS, 2010, 19 (02): : 255 - 261
  • [23] An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks
    Fei Han
    Qing-Hua Ling
    De-Shuang Huang
    Neural Computing and Applications, 2010, 19 : 255 - 261
  • [24] Video deepfake detection using Particle Swarm Optimization improved deep neural networks
    Cunha, Leandro
    Zhang, Li
    Lim, Chee Peng
    Sowan, Bilal
    Kong, Yinghui
    NEURAL COMPUTING & APPLICATIONS, 2024, 36 (15): : 8417 - 8453
  • [25] Chinese Sentiment Classification Model of Neural Network Based on Particle Swarm Optimization
    Zhang, Yaling
    Li, Jiale
    Bai, Shibo
    2020 16TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2020), 2020, : 338 - 341
  • [26] The Research and Application of BP Neural Network Based on Improved Particle Swarm Optimization
    Huang, Dechang
    Huang, Zhaodi
    Zhou, Jiali
    Wang, Yifan
    NEW INDUSTRIALIZATION AND URBANIZATION DEVELOPMENT ANNUAL CONFERENCE: THE INTERNATIONAL FORUM ON NEW INDUSTRIALIZATION DEVELOPMENT IN BIG-DATA ERA, 2015, : 760 - 764
  • [27] An Improved PID Neural Network Control Algorithm Based on Particle Swarm Optimization
    Dou, Chunhong
    Zhang, Ling
    2010 THE 3RD INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND INDUSTRIAL APPLICATION (PACIIA2010), VOL VI, 2010, : 32 - 34
  • [28] Emission Method of Tractor Based on Improved Particle Swarm Optimization Neural Network
    Zheng B.
    Deng Z.
    Luo Z.
    Song Z.
    Mao E.
    Yang Z.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2023, 54 : 417 - 426
  • [29] The optimal combination: Grammatical swarm, particle swarm optimization and neural networks
    de Mingo Lopez, Luis Fernando
    Gomez Blas, Nuria
    Arteta, Alberto
    JOURNAL OF COMPUTATIONAL SCIENCE, 2012, 3 (1-2) : 46 - 55
  • [30] An Improved PID Neural Network Control Algorithm Based on Particle Swarm Optimization
    Dou, Chunhong
    Zhang, Ling
    2011 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION AND INDUSTRIAL APPLICATION (ICIA2011), VOL I, 2011, : 32 - 34
12345