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首页> 外文期刊>Journal of Computers >Novel Learning Algorithm for System Model of Traditional Chinese Drug Fumigation
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Novel Learning Algorithm for System Model of Traditional Chinese Drug Fumigation

机译:中药熏蒸系统模型的新型学习算法

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Using control method to explain medical phenomenon is currently a hot subject of research. The traditional Chinese drug fumigation steaming treat protrusion of protrusion of protrusion of lumbar intervertebral disc with steam generated by boiling medicinal herbs, and this process is a typical non-linear, multivariable, and strong coupling. Experienced nurse and doctor cure patient by their experience. So establish a model of this process can discover more factor of the disease, better treat to protrusion of protrusion of protrusion of lumbar intervertebral disc and reduce of energy consumption. The novel learning algorithm which is combined Ying learning algorithm with fuzzy neural network is proposed in this paper of traditional Chinese drug fumigation fume to cure protrusion of protrusion of protrusion of lumbar intervertebral disc. Proper data pretreatment can improve the accuracy of model. The new way handle of date pretreatment and create a new local space by K-Vector Nearest Neighbors to remove extraneous matter from learning set. This method automatically adjusts fuzzy rules and networks weights based on local space to fit sampling data. The identification model can reveals pathological mechanism of protrusion of protrusion of protrusion of lumbar intervertebral disc. The controller can adjust heater output power based on this model at the state of energy conservation. The simulation results show that the identification model is true and result is feasible. Compared with other methods, the new controller has better dynamics performance and anti-interference capability.
机译:用控制方法解释医学现象是当前研究的热点。传统的熏蒸熏蒸疗法是利用沸腾的药材产生的蒸汽来治疗腰椎间盘突出物的突出物,该过程是典型的非线性,多变量,强耦合性。经验丰富的护士和医生根据他们的经验治愈患者。因此建立该过程的模型可以发现更多的疾病因素,更好地治疗腰椎间盘突出症并降低能耗。提出了一种将Ying学习算法与模糊神经网络相结合的新型学习算法,以治疗腰椎间盘突出症。适当的数据预处理可以提高模型的准确性。日期预处理的新方法,并通过K-Vector最近邻居创建新的局部空间,以从学习集中删除无关的物质。该方法会根据局部空间自动调整模糊规则和网络权重,以适应采样数据。该识别模型可以揭示腰椎间盘突出物突出的病理机制。控制器可以根据该模型在节能状态下调节加热器的输出功率。仿真结果表明,该辨识模型是正确的,结果是可行的。与其他方法相比,新控制器具有更好的动力学性能和抗干扰能力。

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