US 2023/0066478 A1 · 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 (110) for training the complex and non-linear neural network may be equipped with a Machine Learning (ML) Engine (214) to solve the problem efficiently.

Claims26

Dependent claims

A system (110) for training a complex and non-linear neural network,

the system comprising:
a processor (202);
a memory (204) coupled to the processor (202), wherein the memory (204)
comprises processor-executable instructions, which on execution, causes the
processor (202) to:
receive a set of data packets from the neural network;
execute a first set of instructions based on the received set of data packets;
determine a complexity of a region based on the executed first set of instructions;
determine a plurality of sample points proportional to the determined complexity of the region;
determine a plurality of regions of constant complexity based on the plurality of sample points;
and
train the non-linear neural network based on the determined plurality of regions of constant complexity.

9 more elements

Dependencies are inferred from the claim text.

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

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

Life of this patent

Abstract

Claims26