Fuzzy rule systems have found a wide range of applications in many
fields of science and technology. Traditionally, fuzzy rules are
generated from human expert knowledge or human heuristics for relatively
simple systems. In the last few years, data-driven fuzzy rule generation
has been very active. Compared to heuristic fuzzy rules, fuzzy rules
generated from data are able to extract more profound knowledge for more
complex systems. This book presents a number of approaches to the
generation of fuzzy rules from data, ranging from the direct fuzzy
inference based to neural net- works and evolutionary algorithms based
fuzzy rule generation. Besides the approximation accuracy, special
attention has been paid to the interpretabil- ity of the extracted fuzzy
rules. In other words, the fuzzy rules generated from data are supposed
to be as comprehensible to human beings as those generated from human
heuristics. To this end, many aspects of interpretabil- ity of fuzzy
systems have been discussed, which must be taken into account in the
data-driven fuzzy rule generation. In this way, fuzzy rules generated
from data are intelligible to human users and therefore, knowledge about
unknown systems can be extracted.