Influence of the step of changing the training data and the dimensions of the input vector on the accuracy of calculating the load of an unstable two-port network by the neural one

Abstract

The calculations of the load of an unstable two-port network were made. Traditional calculation methods involve laborious parameter redefinition and recalculation of the desired values. In contrast, a neural network includes possible changes in the two-port parameters in the training data. The feedforward neural network training data represents a set of load values and corresponding input current values. Such data are calculated using some change steps of the load and two-port parameters. The training data are split into training, validation, and test sets. It was established that in the training epochs, the neural network reveals that internal pattern in those three sets. Therefore, small errors are obtained. However, the errors appear for the extended control data in different step types. Combining training data with regular and irregular step change parameters eliminates this pattern, so the network shows the capability to generalize. A probability index of the quantification of training quality yields a trade-off between the size of the training data, the accuracy obtained, and the number of neurons. Because an unstable two-port has three parameters, unsatisfactory results were obtained using three base load values. In turn, four excessive base loads radically increase the precision and capability generalization of the network. The established features of the behavior of the neural network provide a base for solving practical “streaming” tasks of different physical nature based on known analogies.