Neural Network Radial Basis Function classifier for earthquake data using aFOA

Machine Learning is a scientific discipline that is concerned with the design and development of computer programs that automatically improves with experience. Machine learning is the popular classification methods in the field of decision support. To design optimal automated classifier to short out the task of classification is necessary, due to number of parameters. This research was limned the motivation for using Artificial Neural Networks with the assistance of evolutionary swarm algorithm such as fruit fly optimization algorithm to be competent tool in finding the most optimal classifier. Research paper presents NN RBF classifier for earthquake data. Proposed Amended Fruit Fly optimization technique improves the performance of classifiers classification accuracy. Research paper analyses and design an optimal classifier using Radial Basis Function Neural Nets.