论文标题
用于预测热量指数的模糊逻辑模型
Fuzzy Logic Model for Predicting the Heat Index
论文作者
论文摘要
开发了一个模糊的推理系统，以预测温度和相对湿度数据的热量指数。模糊逻辑在使用输入到输出的不精确映射到编码系统变量的互连性方面的有效性被利用，以发现一种语言模型，即温度和湿度条件如何影响增长室中的热量指数。在测试集评估时，开发的模型的R2为0.974，RMSE为0.084，结果具有统计学意义（F1,5915 = 222900.858，p <0.001）。通过提供数据趋势的语言摘要以及高预测准确性的优势，被证明是模糊的逻辑模型是一种有效的机器学习方法，可用于热控制问题。
A fuzzy inference system was developed for predicting the heat index from temperature and relative humidity data. The effectiveness of fuzzy logic in using imprecise mapping of input to output to encode interconnectedness of system variables was exploited to uncover a linguistic model of how the temperature and humidity conditions impact the heat index in a growth room. The developed model achieved an R2 of 0.974 and a RMSE of 0.084 when evaluated on a test set, and the results were statistically significant (F1,5915 = 222900.858, p < 0.001). By providing the advantage of linguistic summarization of data trends as well as high prediction accuracy, the fuzzy logic model proved to be an effective machine learning method for heat control problems.
