Designing fuzzy inference systems from data: An interpretability-oriented review
- 1 June 2001
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Fuzzy Systems
- Vol. 9 (3), 426-443
- https://doi.org/10.1109/91.928739
Abstract
Fuzzy inference systems (FIS) are widely used for process simulation or control. They can be designed either from expert knowledge or from data. For complex systems, FIS based on expert knowledge only may suffer from a loss of accuracy. This is the main incentive for using fuzzy rules inferred from data. Designing a FIS from data can be decomposed into two main phases: automatic rule generation and system optimization. Rule generation leads to a basic system with a given space partitioning and the corresponding set of rules. System optimization can be done at various levels. Variable selection can be an overall selection or it can be managed rule by rule. Rule base optimization aims to select the most useful rules and to optimize rule conclusions. Space partitioning can be improved by adding or removing fuzzy sets and by tuning membership function parameters. Structure optimization is of a major importance: selecting variables, reducing the rule base and optimizing the number of fuzzy sets. Over the years, many methods have become available for designing FIS from data. Their efficiency is usually characterized by a numerical performance index. However, for human-computer cooperation another criterion is needed: the rule interpretability. An implicit assumption states that fuzzy rules are by nature easy to be interpreted. This could be wrong when dealing with complex multivariable systems or when the generated partitioning is meaningless for experts. The paper analyzes the main methods for automatic rule generation and structure optimization. They are grouped into several families and compared according to the rule interpretability criterion. For this purpose, three conditions for a set of rules to be interpretable are defined.Keywords
This publication has 53 references indexed in Scilit:
- Alternating cluster estimation: a new tool for clustering and function approximationIEEE Transactions on Fuzzy Systems, 1999
- Reduction of fuzzy rule base via singular value decompositionIEEE Transactions on Fuzzy Systems, 1999
- Improving the interpretability of TSK fuzzy models by combining global learning and local learningIEEE Transactions on Fuzzy Systems, 1998
- A transformed input-domain approach to fuzzy modelingIEEE Transactions on Fuzzy Systems, 1998
- An overview of fuzzy modeling for controlControl Engineering Practice, 1996
- Selecting fuzzy if-then rules for classification problems using genetic algorithmsIEEE Transactions on Fuzzy Systems, 1995
- A possibilistic approach to clusteringIEEE Transactions on Fuzzy Systems, 1993
- Fitting an unknown number of lines and planes to image data through compatible cluster mergingPattern Recognition, 1992
- Orthogonal least squares methods and their application to non-linear system identificationInternational Journal of Control, 1989
- Fast Learning in Networks of Locally-Tuned Processing UnitsNeural Computation, 1989