Predicting Response-Function Results of Electrical/Mechanical Systems Through Artificial Neural Network

TL;DR

This paper introduces a novel use of Artificial Neural Networks to predict response-function results in complex electrical-mechanical systems where traditional methods are ineffective, demonstrating satisfactory performance.

Contribution

The paper presents a new approach using ANN to predict response-functions without prior knowledge, applicable to complex systems beyond electrical circuits.

Findings

ANN accurately predicts response-functions in complex systems

Method works well without explicit response-function knowledge

Applicable to various electrical and mechanical systems

Abstract

In the present paper a newer application of Artificial Neural Network (ANN) has been developed i.e., predicting response-function results of electrical-mechanical system through ANN. This method is specially useful to complex systems for which it is not possible to find the response-function because of complexity of the system. The proposed approach suggests that how even without knowing the response-function, the response-function results can be predicted with the use of ANN to the system. The steps used are: (i) Depending on the system, the ANN-architecture and the input & output parameters are decided, (ii) Training & test data are generated from simplified circuits and through tactic-superposition of it for complex circuits, (iii) Training the ANN with training data through many cycles and (iv) Test-data are used for predicting the response-function results. It is found that the proposed novel method for response prediction works satisfactorily. Thus this method could be used specially for complex systems where other methods are unable to tackle it. In this paper the application of ANN is particularly demonstrated to electrical-circuit system but can be applied to other systems too.