Attributes of the performance of central processing units: a relative performance prediction model
Communications of the ACM
Fuzzy control and fuzzy systems (2nd, extended ed.)
Fuzzy control and fuzzy systems (2nd, extended ed.)
Why triangular membership functions?
Fuzzy Sets and Systems
Simplifying fuzzy rule-based models using orthogonal transformationmethods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A highly interpretable form of Sugeno inference systems
IEEE Transactions on Fuzzy Systems
Constructing fuzzy models with linguistic integrity from numerical data-AFRELI algorithm
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Generating an interpretable family of fuzzy partitions from data
IEEE Transactions on Fuzzy Systems
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
International Journal of Approximate Reasoning
An Inductive Methodology for Data-Based Rules Building
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Identification of transparent, compact, accurate and reliable linguistic fuzzy models
Information Sciences: an International Journal
Learning interpretable fuzzy inference systems with FisPro
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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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.