Abstract
Neural networks are mathematical models that use learning algorithms inspired by the brain to store information. These networks have been extensively used to operate cognitive functions, robustness, fault tolerance, flexibility, collective computation, and ability to deal with fuzzy and inconsistent information. In recent years, probabilistic neural network (PNN) and convolutional neural network (CNN) have been employed in optimization problems such as input normalization and decision making tasks. The flow of data in PNN and CNN will be optimized if its peripherality is minimized. Topological index (TI) is a graph invariant initially introduced to study physiochemical properties of chemical networks. They are further applicable to study certain mathematical aspects of a network such as irregularity, centrality and peripherality. Recently, the Mostar index and edge Mostar index have been introduced to study peripherality of various networks. In the current work, Mostar indices for PNN and CNN have been computed and their application in context of data flow problem together with graphical analysis is presented.