<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In this paper, a method of combining neural network with expert system is proposed, which can realize the control model of real time feedback and control parameters. The model is based on the parameters of production condition, and the current lime quality is predicted. Through the quality of lime and control parameters, using association rule base of expert system, reasoning that lime control parameter adjustment, timely feedback to the lime production control system, to achieve the purpose of real-time control of the quality of lime. The paper presents application of improved BP neural network in hybrid control model of lime quality.</p></abstract>

In this paper, a method of combining neural network with expert system is proposed, which can realize the control model of real time feedback and control parameters. The model is based on the parameters of production condition, and the current lime quality is predicted. Through the quality of lime and control parameters, using association rule base of expert system, reasoning that lime control parameter adjustment, timely feedback to the lime production control system, to achieve the purpose of real-time control of the quality of lime. The paper presents application of improved BP neural network in hybrid control model of lime quality.

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, a method of combining neural network with expert system is proposed, which can realize the control model of real time feedback and control...

NO.	The temperature of exhaust gas (°C)	The temperature of inlet (°C)	The pressure of inlet (Pa)	The temperature of the second air (°C)	The temperature of product (°C)	Activity (ml)	Product state
1	280	1009	-57.78	536	171	360	Over-burned
2	286	1014	-23.64	625	143	351	Over-burned
3	273	983	-25.54	630	95	335	under-burned
…	…	…	…	…	…	…	…
116	270	949	-39.46	578	155	329	under-burned
117	261	954	7.42	571	134	353	under-burned
118	282	1002	-51.04	630	127	365	Over-burned
119	267	967	-20.32	547	109	344	under-burned
120	282	1006	-55.68	628	152	357	Over-burned
…	…	…	…	…	…	…	…
159	286	996	-12.5	627	141	354	under-burned
160	274	949	-12.01	597	112	341	under-burned

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

Publicado en línea: 14 oct 2019

Páginas: 83 - 86

DOI: https://doi.org/10.21307/ijanmc-2019-025

Palabras clave
Improved Neural Network, Lime, Expert System, BP, Hybrid Control

© 2018 Lingli Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

SAMPLE

Application of Improved BP Neural Network in Hybrid Control Model of Lime Quality

Publicado en línea: 14 oct 2019

Páginas: 83 - 86

DOI: https://doi.org/10.21307/ijanmc-2019-025

Palabras claveImproved Neural Network, Lime, Expert System, BP, Hybrid Control

© 2018 Lingli Zhu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

SAMPLE

Palabras clave
Improved Neural Network, Lime, Expert System, BP, Hybrid Control