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Nerve tissues and the nervous system can be modelled into a computer system and which is known as a neural network. A feed-forward neural network is a type of neural network, and these types of functionality are contained in a probabilistic neural network. A probabilistic neural network is extensively applied in the problems of pattern recognition. A system or network's uncertainty is determined by the concept of entropy. Entropy concepts are commonly applied in biology, chemistry, science, engineering in short the applied mathematics. Depending on the circumstances, entropy in a graph's structure can have a variety of shapes. In 1955, graph-based entropy was created. One sort of entropy is weighted-edge entropy. This research looks closely at the abstract form of probabilistic neural networks. We developed few entropies of weighted-edge expressions for a general viewpoint of probabilistic neural networks. On the probabilistic neural network, we measured few weighted-edge and the concepts based on the topological index.

Probabilistic neural network with the concept of edge weight-based entropy