Automated tuning of an electronic circuit board using the artificial neural network approach

Abstract

Manufacturing of electronic circuits for microwave communication boards often requires tuning of different circuit characteristics by manual adjustment of several trimmer components, including the trimmer's resistance and capacitance. This manual tuning process was automated by applying the artificial neural network modeling approach. In the considered tuning process, which required manual adjustment of a set of trimmers, multiple specification criteria had to be satisfied by several trimmer rotations. The tuning process was described in terms of three independent steps: the circuit output measurement, trimmer selection, and trimmer rotation. The trimmer selection was performed by a semi-supervised neural network, which learned the patterns of circuit characteristics and the deviations between the ideal and practical outputs. Another network was developed for determination of trimmer rotation rate. The results, based on computer simulation of the tuning process, showed that the developed system improved performance of the tuning process, allowing for automation of the microwave circuit board tuning task in a real manufacturing environment.

Investigating Injury Occurrence in Motor Vehicle Collision Using Artificial Neural Networks

“…Artificial neural networks (ANN) present a nontraditional alternative to investigating the occurrence of traffic accidents. Research shows that compared with regression models, the versatility and flexibility of an artificial neural network better suits a more in-depth analysis in the area of nonlinear pattern prediction (Chiou, 2006;Eksioglu, Fernandez, & Twomey, 1996;Everitt & Howell, 2003;Fausette, 1994;Karlaftis & Vlahogianni, 2011;Tu, 1996;Wang et al, 2008), intelligent systems (Ukita, Karwowski, Salvendy, Lee, & Zurada, 1996;Zurada, Graham, & Karwowski, 1996), and decision-making process modeling (Hou, Zurada, Karwowski, Marras, & Davis, 2007;Lee, Karwowski, Marras, & Rodrick, 2003). Figure 1 illustrates how a back-propagated architecture works.…”