Computing Irregularity Indices for Probabilistic Neural Network

Shunguang Kang; Yu-Ming Chu; Yu-Ming Chu; Abaid ur Rehman Virk; Waqas Nazeer; Jia Jia

doi:10.3389/fphy.2020.00359

Frontiers in Physics (Sep 2020)

Computing Irregularity Indices for Probabilistic Neural Network

Shunguang Kang,
Yu-Ming Chu,
Yu-Ming Chu,
Abaid ur Rehman Virk,
Waqas Nazeer,
Jia Jia

Affiliations

Shunguang Kang: School of Tourism Data, Guilin Tourism University, Guilin, China
Yu-Ming Chu: Department of Mathematics, Huzhou University, Huzhou, China
Yu-Ming Chu: Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering, Changsha University of Science & Technology, Changsha, China
Abaid ur Rehman Virk: Department of Mathematics, University of Management and Technology, Lahore, Pakistan
Waqas Nazeer: Department of Mathematics, Government College University, Lahore, Pakistan
Jia Jia: School of Tourism Data, Guilin Tourism University, Guilin, China

DOI: https://doi.org/10.3389/fphy.2020.00359
Journal volume & issue: Vol. 8

Abstract

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A topological index (TI) is a quantity expressed as a number that help us to catch symmetry of network. With the help of quantitative structure property relationship (QSPR), we can guess physical and chemical properties of several networks. A neural network is a computer system based on the nerve system. There are numerous uses of these systems in different fields of studies but their most critical use to date is in Neurochemistry. In this paper, we will discuss thirteen irregularity indices for probabilistic neural networks (PNN).

Published in Frontiers in Physics

ISSN: 2296-424X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Science: Physics
Website: https://www.frontiersin.org/journals/physics

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