Title :
A control strategy for electromagnetic near and far field calculation
Author :
Wilk, J. ; Röhm, H.
Author_Institution :
Dept. of Electron. Eng., Univ. of the Fed. Armed Forces, Hamburg, Germany
fDate :
6/23/1905 12:00:00 AM
Abstract :
The ability of neural networks to predict the electromagnetic field of one dimensional sources as well as of two- and three dimensional sources in a wide dynamic range with small error is an imperative condition for use of neural networks in the analysis of complex and fast printed circuit boards. As presented in this paper, Sigma-Pi networks are able to achieve this condition. Because of the high dynamic of the input and the output data within the region of investigation, a wry fast back propagation learning algorithm is used to train the neural network and also presented in this paper. Finally, a short outlook is given to the use of neural networks in analysis of printed circuit boards
Keywords :
backpropagation; circuit analysis computing; circuit complexity; electromagnetic compatibility; electromagnetic field theory; feedforward neural nets; printed circuits; 1D sources; 2D sources; 3D sources; Sigma-Pi networks; back propagation learning algorithm; control strategy; dynamic; electromagnetic far field calculation; electromagnetic field; electromagnetic field sources; electromagnetic near field calculation; feedforward Sigma-Pi networks; input data; neural network training; neural networks; output data; printed circuit board analysis; Dynamic range; Electromagnetic analysis; Electromagnetic compatibility; Electromagnetic fields; Impedance; Message-oriented middleware; Neural networks; Printed circuits; Time measurement; Transmission line matrix methods;
Conference_Titel :
Electronics, Circuits and Systems, 2001. ICECS 2001. The 8th IEEE International Conference on
Print_ISBN :
0-7803-7057-0
DOI :
10.1109/ICECS.2001.957458
