Neural Compensation for a Microcontroller Based Frequency Synthesizer-Vector Voltmeter

An automated neural network compensation scheme is proposed for an indigenously developed microcontroller based frequency synthesizer-vector voltmeter system, developed using direct digital synthesis and the synchronous detection technique. This compensator, when implemented online, can significantly improve the reading of an unknown voltage (both in magnitude and phase), in real-time. The neural compensator developed is trained offline on the basis of real data acquired from the system, and when this compensator is implemented online, it could outperform polynomial and fuzzy based compensators for a variety of different unknown voltages under measurement.