earticle

영어

Non-linearity coupled by all of the sensors and transducers gives set to either difficulties for direct digital readout, on-chip interface, testing, calibration and control. Also, the performance of a transducer is affected adversely by variations in working environments over them. Under the sovereignty of ANN based transducer modeling, the use of single layer ANN has been proposed in two separate studies with quite affluent results. The first existing model is based on the architecture of an adaptive linear (ADALINE) network trained with Widrow-Hoff’s learning algorithm. The other is based on the concept of Functional Link Artificial Neural Network (FLANN) designed on the architecture of a single layer linear ANN trained with Gradient-descent with momentum based learning algorithm. To have an optimal solution, it is proposed to amalgamate the direct model of the transducers using the concept of a Polynomial-ANN trained with BFGS Quasi-Newton Learning algorithm. The proposed Polynomial-ANN oriented transducer model is implemented based on the topology of a single-layer feed-forward back-propagation network. The proposed transducer modeling technique provides an extremely fast convergence speed with increased accuracy for the assessment of static input-output characteristics of the transducers and also for the solution of linearizing the non-linear transducers.