  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.
