An Extreme Learning Machine for the Simulation of Different Hysteretic Behaviors

Hysteresis is a non-unique phenomenon known as a multi-valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a novel approach for simulating hysteresis with various features that combines the extreme learning machine (ELM) and least-squares support vector machine (LS-SVM). First, the hysteresis is converted into a single-valued mapping by deteriorating stop operators, a combination of stop and play hysteresis operators. Then, the converted mapping is learned by a LS-SVM model. This approach facilitates the training steps and provides more accurate results in contrast to the previous experimental studies. The proposed model is evaluated for several hystereses with various properties. These properties include rate-independent or rate-dependent, congruent or non-congruent, and symmetric or asymmetric problems. The results indicate the efficiency of the newly developed technique in terms of accuracy, computational cost, and convergence rate.

Automated summary

This study proposes a novel approach combining extreme learning machine (ELM) and least-squares support vector machine (LS-SVM) for simulating hysteresis with different features. The approach accurately identifies hysteretic systems and provides more accurate results with lower computational cost compared to previous experimental studies.