Analysis and Classification of Methods of Neural and Phase Logic for the Development of Parametric Identification Algorithms for Control Objects

被引：0
|
作者：
A. E. Polyakov
E. M. Filimonova
P. M. Mukhina
机构：
[1] Kosygin Russian State University,
来源：
Fibre Chemistry | 2018年 / 49卷
关键词：
Control Objective; Parameter Identification Algorithm; External Friction Forces; Recursive Neural Network; Fuzzy Logic Apparatus;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A general algorithm for identification of fibrous materials based on expert knowledge with an appropriate selection of the number and form of the membership functions of the fuzzy sets used in the model is determined. The basic methods of parametric identification of a control object is considered, the first of which is based on a structural model of the object while the second uses only a training sample.
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页码：408 / 410
页数：2
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