摘要
针对迟滞的非光滑、多值映射特性进行分析,提出了反映迟滞主要特征的迟滞算子的构造方法,并将迟滞算子引入扩展输入空间,实现了将迟滞的多值映射转换为一一映射,建立了关于迟滞的神经网络模型.避免了对非光滑的迟滞求梯度,获得了简单的模型结构和较高的建模精度.
An approach to construct the hysteretic operator is proposed based on the analysis on the characteristic of hysteresis with non-smooth and multi-valued mapping. By introducing the proposed hysteretie operator into the expanded input space, the multi-valued mapping of hysteresis can be transferred into a one-to-one mapping. Thus, the neural network based model can be derived based on the expanded input space. This method avoids the calculation of gradients of the non-smooth hysteresis with respect to its input. Then, an accurate model with simpler architecture has been obtained.
出处
《上海师范大学学报(自然科学版)》
2009年第3期229-236,共8页
Journal of Shanghai Normal University(Natural Sciences)
基金
上海师范大学重学科项目(DZL811)
上海市教委科技创新重点项目(09ZZ141)
上海师范大学前瞻性项目(DYL200809)
国家自然科学基金(60572055)
关键词
迟滞
非光滑系统
扩展输入空间
模型
Hysteresis
non-smooth system
expanded input space
model