Intelligent Modeling and Control of a Conveyor Belt Grain Dryer Using a Simplified Type 2 Neuro-Fuzzy Controller

被引：25

作者：

Lutfy, Omar F. ^{[1
]}

Selamat, Hazlina ^{[2
]}

Noor, S. B. Mohd ^{[3
]}

机构：

[1] Univ Technol Baghdad, Control & Syst Engn Dept, Baghdad 18310, Iraq

[2] Univ Teknol Malaysia, Ctr Artificial Intelligence & Robot, Kuala Lumpur, Malaysia

[3] Univ Putra Malaysia, Dept Elect & Elect Engn, Serdang, Malaysia

来源：

DRYING TECHNOLOGY | 2015年 / 33卷 / 10期

关键词：

Conveyor belt grain dryer; Genetic algorithm; System identification; Type 2 neuro-fuzzy controller; LEAVES ILEX-PARAGUARIENSIS; RESEARCH-AND-DEVELOPMENT; DEEP-BED; NONLINEAR IDENTIFICATION; MOVING-BED; NETWORK; QUALITY; PADDY; SIMULATION; ENERGY;

D O I：

10.1080/07373937.2015.1021007

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

In this article, a nonlinear autoregressive with exogenous input (NARX) network was utilized to model a conveyor belt grain dryer using a set of input-output data collected during an experiment to dry paddy grains. The resulting NARX model has achieved a remarkable modeling accuracy compared to other previously reported modeling techniques. To control the considered dryer, a simplified type 2 adaptive neuro-fuzzy inference system (ANFIS) controller was proposed. The effectiveness of this controller was demonstrated by several performance tests conducted by computer simulations. Moreover, a comparative study with other related controllers further confirmed the superiority of the proposed dryer controller.

引用

页码：1210 / 1222

页数：13

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