  In many fields where human understanding plays a crucial role, such as
bioprocesses, the capacity of extracting knowledge from data is of critical
importance. Within this framework, fuzzy learning methods, if properly used,
can greatly help human experts. Amongst these methods, the aim of orthogonal
transformations, which have been proven to be mathematically robust, is to
build rules from a set of training data and to select the most important ones
by linear regression or rank revealing techniques. The OLS algorithm is a good
representative of those methods. However, it was originally designed so that it
only cared about numerical performance. Thus, we propose some modifications of
the original method to take interpretability into account. After recalling the
original algorithm, this paper presents the changes made to the original
method, then discusses some results obtained from benchmark problems. Finally,
the algorithm is applied to a real-world fault detection depollution problem.
