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Particle Swarm Optimization-Neural Network Algorithm and Its Application in the Genericarameter of Microstrip Line

  • Guangbo Wang
  • Jichou Huang
  • Pengwei Chen
  • Xuelian Gao
  • Ya Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

Abstract

To solve the general model for S-parameter of microstrip line quickly, this paper proposes Particle Swarm Optimization-Neural Network (PSO-NN) algorithm, which is based on the research of Particle Swarm Optimization (PSO) algorithm and neural network algorithm. By testing and analyzing PSO-NN, PSO and BP neural network algorithm respectively with the performance check function, we find PSO-NN the best performance. Finally, PSO-NN algorithm is applied to the general model for S-parameter of microstrip line which has made use of CST software to get the training data and validation data of the S-parameter of microstrip line. By training and validating PSO-NN, PSO and BP neural network algorithm, we prove that PSO-NN algorithm has the minimum average error and standard deviation in acceptable time. Compared with CST software, the PSO-NN algorithm has shorter simulation time at the same precision level .Therefore, this paper is of great value to the research of PCB board.

Keywords

Particle Swarm Optimization-Neural Network (PSO-NN) algorithm S-parameter of microstrip line Algorithms Performance Check Function 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guangbo Wang
    • 1
  • Jichou Huang
    • 1
  • Pengwei Chen
    • 1
  • Xuelian Gao
    • 1
  • Ya Wang
    • 1
  1. 1.School of Electrical and Electronic EngineeringNorth China Electric Power UniversityBeijingChina

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