Development of a virtual linearizer for correcting transducer static nonlinearity

This paper reports the development of an artificial neural network based virtual linearizer for correcting nonlinearity associated with transducers connected to the data-acquisition system of a computer-based measurement system. In analog processing techniques, nonlinearity is considered to be a very serious problem that at one time was solved frequently by the piecewise linear segment approach modeled by linear electronic circuits. Since the cost of microcomputers has been reduced drastically, they are currently used in most applications of measurement, including data-acquisition subsystems. Therefore, the hardware-based analog techniques of linearization are often replaced by the software-based numerical ones. In this context, it has been found that a multilayer feed-forward back-propagation network trained with the Levenberg-Marquardt learning rule provides an optimal solution to implement an efficient soft compensator to correct transducer static-nonlinearity.