Journals Information
Mathematics and Statistics Vol. 12(2), pp. 175 - 183
DOI: 10.13189/ms.2024.120207
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Product Signed Domination in Probabilistic Neural Networks
T. M. Velammal 1,*, A. Nagarajan 2, K. Palani 3
1 Research Scholar, Registration Number: 21212232092010, PG & Research Department of Mathematics, V.O. Chidambaram College, Thoothukudi-628008, Tamil Nadu, India. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamil Nadu, India
2 Head & Associate Professor (Retd.), PG & Research Department of Mathematics, V.O. Chidambaram College, Thoothukudi-628008, Tamil Nadu, India. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamil Nadu, India
3 Head & Associate Professor, PG & Research Department of Mathematics, A.P.C. Mahalaxmi College For Women, Thoothukudi-628002, Tamil Nadu, India. Affiliated to Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli-627012, Tamil Nadu, India
ABSTRACT
Domination plays a very important role in graph theory. It has a lot of applications in various fields like communication, social science, engineering, etc. Let 
be a simple graph. A function 
is said to be a product signed dominating function if each vertex 
in 
satisfies the condition 
where 
denotes the closed neighborhood of . The weight 
of a function 
is defined as 
. The product signed domination number of a graph 
is the minimum positive weight of a product signed dominating function and is denoted as 
. Product signed dominating function assigns 1 or -1 to the nodes of the graph. This variation of dominating function has applications in social networks of people or organizations. Probabilistic Neural Network (PNN) was first proposed by Specht. This is a classifier that maps input patterns in a number of class levels and estimates the probability of a sample being part of learned theory. This paper studies the existence of product signed dominating functions in probabilistic neural networks and calculates the accurate values of product signed domination numbers of three layered and four layered probabilistic neural networks.
KEYWORDS
Product Signed Dominating Function, Product Signed Domination Number, Probabilistic Neural Networks
Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] T. M. Velammal , A. Nagarajan , K. Palani , "Product Signed Domination in Probabilistic Neural Networks," Mathematics and Statistics, Vol. 12, No. 2, pp. 175 - 183, 2024. DOI: 10.13189/ms.2024.120207.
(b). APA Format:
T. M. Velammal , A. Nagarajan , K. Palani (2024). Product Signed Domination in Probabilistic Neural Networks. Mathematics and Statistics, 12(2), 175 - 183. DOI: 10.13189/ms.2024.120207.