A recurrent self-evolving fuzzy neural network predictive control for microwave drying process
2016
Li, Jianshuo | Xiong, Qingyu | Wang, Kai | Shi, Xin | Liang, Shan
A recurrent self-evolving fuzzy neural network (RSEFNN) predictive control scheme is developed for microwave drying process in this paper. During microwave drying process, the temperature, power absorption efficiency, and moisture variation characteristic in the drying material cannot be exactly known for the complex application environment. So a RSEFNN is constructed to predict the microwave drying process. Based on the RSEFNN, to achieve a highly efficient and safe microwave drying process, a multiple objectives predictive control algorithm is constructed to get a suitable input power over a prediction horizon. To identify the feasibility of the proposed recurrent self-evolving fuzzy neural network predictive control (RSEFNNPC) algorithm, a simulation of Red Maple and an actual application of lignite drying were analyzed in this paper. In the Red Maple drying process, temperature and moisture content are chosen as control objectives. As the simulation results show, the RSEFNNPC could achieve multiple objectives optimization. In the actual lignite drying process, the difference between lignite temperature and presupposed temperature was below 2 K. The difference between RSEFNN prediction and actual sampling temperature was below 1 K.
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Эту запись предоставил National Agricultural Library