Virtual curve tracer for estimation of static response characteristics of transducers

The paper proposed a new practical approach for optimal fitting of transducer characteristics to measured data using artificial neural network (ANN) based virtual curve tracer (VCT). The performance of the implemented system is examined experimentally for an industrial grade pressure transducer, connected across a data acquisition system (DAS) of a computer based measurement system. The core of applications used ANN architectures, based on multilayer perceptrons, trained with back-propagation learning algorithm, as solutions to transducer characteristic interpolation. However, a number of different variants of the standard basic gradient descent back-propagation learning algorithm, for training the multilayer perceptrons, are reported in the literature. But there are no specific rules to select the best learning algorithm for a given set of input-output pairs, where transducer non-linearity is the main factor to be considered. In this context, we present a comparative evaluation of the relative performance of different multilayer perceptron based models for optimal fitting of transducer static response characteristics, with particular attention paid to the speed of computation, accuracy achieved, architectural complexity and computational load for a given set of training data. This type of performance comparison has not been attempted so far.