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Modeling and Optimization of Coal Moisture Control System Based on BFO

机译:基于BFO的煤水分控制系统的建模与优化。

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Coal moisture control process is a critical process in energy saving for pollution reduction and improving production efficiency and the quality of coke. The RBF artificial neural network approach for modeling is used to achieve precise control of coal moisture control system and against their strong coupling nonlinear systems with time-delay characteristics. The bionic BFO (Bacterial Foraging Optimization) is used to the fitness to optimize the RBF Neural network parameters. In order to achieve better results the RBF Neural network performance is optimized by these bionic BFO. This method provides a theoretical basis for accurate control of coal moisture process. The reduction of energy and pollution with improving the quality of coke is established.
机译:煤的水分控制过程是节能减排,提高生产效率和焦炭质量的关键过程。运用RBF人工神经网络建模方法来实现对煤水分控制系统的精确控制,并克服了具有时滞特性的强耦合非线性系统。仿生BFO(细菌觅食优化)用于优化RBF神经网络参数。为了获得更好的结果,这些仿生BFO优化了RBF神经网络性能。该方法为准确控制煤水分过程提供了理论依据。建立了通过改善焦炭质量来​​减少能源和污染的技术。

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