Hysteresis modeling of antiferroelectric micro-cantilever with anticommutative multilayer perceptrons

Abstract

The discrete Preisach model is popular to describe the hysteresis of smart materials, such as the piezoelectric and ferroelectric material. Re-explaining the discrete Preisach model of antiferroelectric materials after introducing an extended Preisach function, the special multilayer perceptrons with only two input nodes was designed intrinsically to satisfy the anticommutative property of hysteresis without superfluous hidden nodes (called aMLP), where its output equals to the difference between two predictive results that two signals in the normal and reverse sequence are imported to the aMLP model. Several training algorithms, including the normal/hybrid generic algorithm (GA) and the normal/hybrid particle swarm optimization (PSO), were used to adjust the parameters of the aMLP model, respectively. The comparing results indicate that the aMLP models designed with the winner operation function pair of [tanh, ReLU] and trained by both normal and hybrid PSO algorithms suggest the better performance when applying the working voltages more than 50 V according to the experimental data. It provides another accurate way for an aMLP model to estimate the anticommutative property of hysteresis of smart materials.

Keywords:

• Hysteresis of antiferroelectric materials
• Preisach model
• anticommutative property
• multilayer perceptrons
• particle swarm optimization

Acknowledgments

The authors would like to express our gratitude to Prof. Xiujian Chou for his support and materials used for experiments.

Conflict of interest statement

We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, this manuscript.

Additional information

Funding

This work was supported by National Natural Science Foundation of China under Grant No. 61774138, and the Shanxi Provincial Natural Science Foundation of China under Grant No. 201801D121184.