摘要
一位新闻记者兼机器人与机器学习每日新闻的工作人员新闻编辑在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者从中华人民共和国哈尔滨发回的消息，研究表明：“电机的温度对电机的控制和寿命有很大影响。然而，由于电机结构和运行环境的影响，用温度传感器精确测量电机的温度具有挑战性。”我们的新闻记者从哈尔滨大学的研究中获得了一句话：“因此，机器学习算法经常被用来更准确地预测温度。为了加强电机控制，将机器学习算法模型与实际电机控制终端相结合是非常有益的，本文提出了一种基于Har Ris Hawk优化的短、长记忆(LSTM)算法模型来预测电机定子温度，并将其应用于实际电机控制中，对电机控制电流的跟踪性能进行了评估。建立了一个温度测量实验平台,采集电机不同位置的温度作为原始数据,然后将原始数据输入到LSTM、PSO-LSTM和HO-LSTM三种算法中进行比较,通过比较评价方法,证明HO-LSTM具有良好的预测性能。利用电机的不同段作为模型输入集，提高了模型的泛化能力和预测精度。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – Research findings on Machine Learning are discuss ed in a new report. According to news originating from Harbin, People’s Republic of China, by NewsRx correspondents, research stated, “The temperature of a moto r significantly affects its control and lifespan. However, due to the influence of motor structure and operating environment, precise temperature measurement of the motor is challenging with temperature sensors.” Our news journalists obtained a quote from the research from Harbin University, “Therefore, machine learning algorithms are often employed to predict the temper ature more accurately. To enhance motor control, integrating machine learning al gorithm models with the actual motor control terminal is highly beneficial. This paper proposes a Short and Long Term Memory (LSTM) algorithm model based on Har ris’s hawk optimization to predict the temperature of the motor stator, which is applied in actual motor control. Furthermore, it evaluates the tracking perform ance of motor control current. Firstly, an experimental platform for temperature measurement is established to acquire the temperature at different positions of the motor as raw data. Subsequently, the raw data is inputted into three algori thms: LSTM, PSO-LSTM, and HHO-LSTM, for comparison. By comparing evaluation metr ics, it is demonstrated that HHO-LSTM exhibits excellent predictive performance. Furthermore, utilizing diverse segments of the motor as model input sets enhanc es the generalization capability and predictive accuracy of the model.”
NETL
NSTL