An RBF Neural Network Clustering based on Naive Bayes Algorithm

S.M. Abirami

G.S Blessing Antony

P. Promoth Mahajan

B. Sneka

Dr.S. Nithya Kalyani

Keywords: FMM, Neural Network, RBF.


A controlled classification calculation called a brain network can handle highly complex and nonlinear information analysis. A few well-known names are required for the administered calculation's preparation cycle, and boundaries are then fixed using the back-propagation technique. Not with standing, current writing generally uses Auto-Encoder to lessen the information component when searching for bunching troubles due to the lack of stamped marks. This task suggests a brain network grouping calculation using RBF (Spiral Premise Capability) in light of the FMM theory, which first uses the FMM calculation for pre classification and then creates self-regulated markings in light of the FMM hypothesis for back propagation. The calculation presented in this research fits with a self-regulated brain network bunching calculation and also gives the brain network the ability to self-direct and self-improve in real life. It very well may be shown that the proposed computation has incredible versatility and vigor from the exploratory results of the fake informational indexes and the UCI informational indexes.