Recurrent Polynomial and Neural Structures in Modelling of a Neutralisation Process

被引：1
|
作者：
Chaber, Patryk [1 ]
Lawrynczuk, Maciej [1 ]
机构：
[1] Warsaw Univ Technol, Inst Control & Computat Engn, Ul Nowowiejska 15-19, PL-00665 Warsaw, Poland
来源：
PROGRESS IN AUTOMATION, ROBOTICS AND MEASURING TECHNIQUES: CONTROL AND AUTOMATION | 2015年 / 350卷
关键词：
recurrent dynamic models; multi layered perceptron; polynomial model; neural model; pH neutralisation;
D O I：
10.1007/978-3-319-15796-2_3
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work discusses modelling of a neutralisation process by means of two recurrent modelling techniques: polynomials and neural networks. Model structures and training algorithms are shortly discussed. Two recurrent model classes are compared in terms of accuracy and complexity. Advantages of neural models are emphasised.
引用
收藏
页码：23 / 32
页数：10
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