Title :
RBF Intelligent Model of Microwave Rectangular PDGS-LPF
Author :
Jin, TaoBin ; Jin, Jie ; Yao, Ruipu ; Li, Kejia ; Zhang, Yizhen
Author_Institution :
Sch. of Inf. Eng., Tianjin Univ. of Commerce, Tianjin, China
Abstract :
Periodic defected ground structures (PDGS) with rectangular defected areas has excellent low-pass properties when periodic unit amounts and structure sizes meet definite conditions. Microwave low-pass filter (LPF) is a kind of device which can separate different signals within microwave frequency range, widely used in microwave communication system. The precise transmission coefficient (S21) of rectangular PDGS is obtained through FDTD analysis. The results of FDTD simulation and measurement in the references verify the validity of the FDTD method. The influences of lattice dimensions on the characteristic of frequency are studied. According to RBF artificial neural network (ANN) theory, RBF intelligent model of microwave PDGS-LPF with etched rectangles is developed for the first time on the basis of FDTD analysis. The structures sizes of rectangular PDGS-LPF and the frequency are defined as the input samples of the RBF, transmission coefficient are defined as the output samples. After the RBF has been successfully trained with improved Gaussian algorithm, S21 at any arbitrary parameters can be obtained quickly from RBF intelligent model within training values range. Finally, RBF intelligent model has been approved by FDTD results. It is also showed that RBF intelligent model is very effective, which will provide powerful approach for the precise analysis and quick design of rectangular PDGS-LPF.
Keywords :
artificial intelligence; electrical engineering computing; finite difference time-domain analysis; microwave filters; neural nets; radial basis function networks; FDTD analysis; Gaussian algorithm; RBF intelligent model; artificial neural network; microwave communication system; microwave low pass filter; microwave rectangular PDGS LPF; periodic defected ground structure; transmission coefficient; Artificial neural networks; Finite difference methods; Microwave circuits; Microwave filters; Periodic structures; Time domain analysis; Training;
Conference_Titel :
Wireless Communications Networking and Mobile Computing (WiCOM), 2010 6th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-3708-5
Electronic_ISBN :
978-1-4244-3709-2
DOI :
10.1109/WICOM.2010.5600102