Using AIG in Verilog HDL, Autonomous Testing in a Family of Wien Bridge Cross Transducers

Abstract

This research reports on the software configuration of automated fault detection and recognition using neural networks (ANNs) in a class of 13.6 cross actuators. According to the findings, the suggested flaw detector is ideal for integrating knowledge into the devices in a way that is living thing. The seven often recurring defects in a batch of these sensors are directly determined by the automated fault tester that is being demonstrated. In this study, the suggested automated defect detector is trained using an ANN-based binary class system. If any of the mistakes occurs, logic Programming is applied to define a high or “1” output, whereas the returning is calculated whether the other 6 failures occurred lowest or “0”. The input outputs from the Or CAD programme are used as incoming signal, and indeed the produced train parameters, i.e., amplitude and biased of the artificial neural tool of Math, have been used.