Diagonal Recurrent Neural Network-Based Hysteresis Modeling

The Preisach model and the neural networks are two of the most popular strategies to model hysteresis. In this article, we first mathematically prove that the rate-independent Preisach model is actually a diagonal recurrent neural network (dRNN) with the binary step activation function. For the first time, the hysteresis nature and conditions of the classical dRNN with the tanh activation function are mathematically discovered and investigated, instead of using the common black-box approach and its variants. It is shown that the dRNN neuron is a versatile rate-dependent hysteresis system under specific conditions. The dRNN composed of those neurons can be used for modeling the rate-dependent hysteresis and it can approximate the Preisach model with arbitrary precision with specific parameters for rate-independent hysteresis modeling. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.

Automated summary

This article mathematically proves that the rate-independent Preisach model is a dRNN neural network and investigates the hysteresis nature and conditions of the classical dRNN with tanh activation function. Experiments show that the classical dRNN models both kinds of hysteresis more accurately and efficiently than the Preisach model.