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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bai, X., Xu, L.J.: Research on the Effects of Substrates on the Crosstalk in Microstrips. Journal of Electronics & Information Technology 32(11), 2768 (2010) (in Chinese)
An, J., Wu, J.F., Wu, Y.H.: Research of Suppressing Crosstalk of the Microstrip Lines by Using Stripe Protection. Transactions of Beijing Institute of Technology 31(3), 344 (2011) (in Chinese)
Liu, Q.: Interface Between C++Builder and CST Microwave Studio Andits Application Based on OLE. Journal of Computer Applications 32(z1), 77 (2012) (in Chinese)
Zhang, F., Du, X.Y., Zhou, D.F.: Application of Software CST to the Design of LPDA. Computer Applications and Software 24(11), 16 (2007) (in Chinese)
Liu, L.L., Wang, Y.Q.: Generalized Transmission Lline Equations Combined with Pade Approximation for the Analysis of Signal Iintegrity. Journal of Nanjing University of Posts and Telecommunications(Natural Science)Â 31(6), 17 (2011) (in Chinese)
Wang, H.Y., Zhang, X.Z.: Study on Coupling Between Microstrip and Patch Antenna in FDTD Method. Journal of Harbin University of Commerce(Natural Sciences Edition)Â 27(2), 219 (2011) (in Chinese)
Sun, R.H., Gao, W.D., Liu, H.: Analysis on the Characteristics of CSRR Microstrip Based on FDTD. Journal of Anhui University (Natural Sciences)Â 36(1), 76 (2012) (in Chinese)
Wang, K.W., Wang, J.H.: Study on the Transmission and Renection Properties 0f the Microstrip Line Bend. Journal of Microwaves 22(3), 32 (2006) (in Chinese)
Yuan, Z.H., Geng, J.P., Jin, R.H.: Pattern Synthesis of 2-D Arrays Based on a Modified Particle Swarm Optimization Algorithm. Jounal of Electronics&Information Technology 29(5), 1236–1237 (2007) (in Chinese)
Hu, L.L., Guo, Y.C.: An Orthogonal Wavelet Transform Blind Equalization Algorithmbased on the Optimization of Particle Swarm. Jounal of Electronics&Information Technology 33(5), 1254 (2011) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, G., Huang, J., Chen, P., Gao, X., Wang, Y. (2013). Particle Swarm Optimization-Neural Network Algorithm and Its Application in the Genericarameter of Microstrip Line. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-39482-9_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39481-2
Online ISBN: 978-3-642-39482-9
eBook Packages: Computer ScienceComputer Science (R0)