The Classification of a Simulation Data of a Servo System via Evolutionary Artificial Neural Networks

Abstract

Evolutionary neural networks (EANNs) are the combination of artificial neural networks and evolutionary algorithms. This merge enabled these two methods to complement the disadvantages of the other methods. Traditional artificial neural networks based on backpropagation algorithms have some limitations. Contribution by artificial neural networks was the flexibility of nonlinear function approximation, which cannot be easily implemented with prototype evolutionary algorithm. On the other hand, evolutionary algorithm has freed artificial neural networks from simple gradient descent approaches of optimization. Classification is an important task in many domains and though there are several methods that can be used to find the relationship between the input and output space , among the different works, EAs and NNs stands out as one of the most promising methods. In this study, the data gathered from a simulation of a servo system involving a servo amplifier, a motor, a lead screw/nut, and a sliding carriage of some sort is classified by the application coded in Qt programming environment to predict the rise time of a servomechanism in terms of two (continuous) gain settings and two (discrete) choices of mechanical linkages.