An efficient inverse ANN modeling approach using prior knowledge input with difference method

Abstract

Artificial neural networks (ANN) have emerged as a powerful technique for modeling. Since the embedding knowledge in ANN models is possible by the knowledge based ANN (KBANN) methods, more accurate results than classical ANN approach can be obtained with KBANN. Source difference (SD), prior knowledge input (PKI) and prior knowledge input with difference (PKI-D) are several methods to be mentioned which combines existing knowledge with ANN methods. The existing knowledge is obtained either by mathematical formulations, ANN modeling or measured data. The prior knowledge input with difference, which is the latest method amongst KBANN approaches is discussed in this work. We compared the response efficiency and time consumption performances of PKI-D and classical ANN methods to obtain model for inverse scattering problem.