Fuzzy Logic Model for Predicting the Heat Index
This provides an effective machine learning method for heat control problems, but it is incremental as it applies an existing fuzzy logic approach to a specific domain.
The researchers tackled the problem of predicting the heat index from temperature and humidity data by developing a fuzzy inference system, achieving an R2 of 0.974 and RMSE of 0.084 on a test set.
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.