Genetic Algorithm Based Neuro-fuzzy Network Adaptive PID Control and Its Applications

Dong-qing, Feng; Dong, Lei; Fei, Minrui; Chen, Tiejun

doi:10.1007/978-3-540-30497-5_52

Cited by 4 publications

(1 citation statement)

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“…Previous research on the tobacco curing control process mainly focused on how to automatically implement temperature control in bulk curing barns. Fuzzy-based control methods have been mostly employed [4,6,7]. These methods keep the measured DBT or WBT in the curing barn consistent with the set DBT or WBT of the bulk curing schedule.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

Yang

2019

Sensors

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The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

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Section: Introductionmentioning

confidence: 99%