Method and system for learning behavior of highly complex and non-linear systems

Abstract

The present disclosure generally relates to handling data of non-linear, multi-variable complex systems. More particularly, the present disclosure relates to methods and systems for training machine learning-based computing devices to ensure adaptive sampling of highly complex data packets. The present invention provides a robust and effective solution to implement a complexity-based sampling methodology that trains the neural network in complex mapping regions, by iteratively sampling the DBMS function and training the neural network in complex regions. The system () for training the complex and non-linear neural network may be equipped with a Machine Learning (ML) Engine () to solve the problem efficiently.