Hybrid fuzzy convolution modelling and identification of chemical process systems

被引：7

作者：

Abonyi, J ^{[1
]}

Nagy, L ^{[1
]}

Szeifert, F ^{[1
]}

机构：

[1] Univ Veszprem, Dept Chem Engn Cybernet, H-8201 Veszprem, Hungary

来源：

INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE | 2000年 / 31卷 / 04期

关键词：

D O I：

10.1080/002077200291046

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 paper looks at a new method of modelling nonlinear dynamic processes, using grid-type Takagi-Sugeno fuzzy models and a priori knowledge. The proposed hybrid fuzzy convolution dynamic model consists of a non-linear fuzzy steady-state static and a gain-independent impulse response model-based dynamic part. The modelling of nonlinear pH processes is chosen as a realistic case study for demonstration of the proposed modelling approach. The off-line identified hybrid fuzzy convolution model is shown to be capable of modelling the nonlinear process and providing better multiple-step prediction than the conventional grid-type Takagi-Sugeno fuzzy model.

引用

页码：457 / 466

页数：10

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