A Review on Enhancing the Linearity Characteristic of Different Types of Transducers-A Comparative Study (original) (raw)

Abstract: Many types of sensors and transducers have a nonlinear response. Ideal transducers are designed to be linear. But since in practice there are several factors which introduce non-linearity in a system. Due to such nonlinearities, transducer’s usable range gets restricted and also accuracy of measurement is severely affected. Similar effect is observed in different types of transducers. The nonlinearity present is usually time-varying and unpredictable as it depends on many uncertain factors. Nonlinearity also creeps in due to change in environmental conditions such as temperature and humidity. In addition ageing of the transducers also introduces nonlinearity. This particular paper concentrates a review on the compensation of difficulties faced due to the non-linear response characteristics of different types of sensors like resistive (thermocouple), capacitive (capacitive pressure sensor),inductive(LVDT) and humidity transducers. In this review, we identified many algorithms and ANN models like Functional Link Artificial Neural Network (FLANN), Radial Basis Function based ANN, Multi Layer Perceptron and Back Propagation Network to enhance the linearity performance of resistive, capacitive and inductive transducers. On comparison of different ANN models for non-linearity correction in different types of transducers, we identified FLANN model is used as a useful alternative to the MLP, BPN and the radial basis function (RBF)-based ANN. It has the advantage of lower computational complexity than the MLP, BPN and RBF structures and is, hence, easily implementable. Throughout the paper, we described the effects produced by each kind of nonlinearity, emphasizing their variations for different types of transducers with different ANN models.