Title :
A new neural networks-based model of hysteresis
Author :
Lianwei Ma ; Yu Shen ; Jinrong Li ; Xinlong Zhao
Author_Institution :
Sch. of Autom. & Electr. Eng., Zhejiang Univ. of Sci. & Technol., Hangzhou, China
Abstract :
A new approach to constructing hysteretic operator is proposed in this paper. Based on the hysteretic operator, the input space of neural networks is expanded from 1-dimension to 2-dimension and the multi-value mapping of hysteresis is transformed into one-to-one mapping. Based on the expanded input space, a neural network is employed to approximate hysteresis. The result of an experimental example suggests the proposed approach is effective.
Keywords :
approximation theory; hysteresis; neural nets; expanded space method; hysteretic operator; multivalue mapping; neural network; Artificial neural networks; Biological neural networks; Data models; Hysteresis; Neurons; Predictive models; expanded space method; hysteresis; hysteretic operator; neural networks;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7052948
