FisPro(Fuzzy Inference System Professional) allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system. Fuzzy inference systems are briefly described in the fuzzy logic glossary given in the user documentation. They are based on fuzzy rules, which have a good capability for managing progressive phenomenons. Fuzzy logic, since the pioneer work by Zadeh, has proven to be a powerful interface between symbolic and numerical spaces. One of the reasons for this success is the ability of fuzzy systems to incorporate human expert knowledge with its nuances, as well as to express the behaviour of the system in an interpretable way for humans. Another reason is the possibility of designing data-driven FIS to make the most of available data. Despite these assets, using FIS as a collaborative framework for system modelling has not been paid as much attention as it deserves, and this ascertainment was our main incentive for starting the FisPro project a few years ago. With this in mind, we concentrated our efforts on three points: The rule base interpretability. This is the main originality of FisPro, as interpretability is guaranteed in each step of the design: variable partitioning, rule induction, rule base simplification, optimization. A modular, portable software architecture that allows platform independence and facilitates extension writing. A free and open source software, licensed to grant the right of users to use, study, change, and improve its design through the availability of its source code.
Keywords for this software
References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Tsekouras, George E.: Fuzzy rule base simplification using multidimensional scaling and constrained optimization (2016)
- Troiano, Luigi; Rodríguez-Muñiz, Luis J.; Marinaro, Pasquale; Díaz, Irene: Statistical analysis of parametric t-norms (2014)
- Guillaume, Serge; Charnomordic, Brigitte; Loisel, Patrice: Fuzzy partitions: a way to integrate expert knowledge into distance calculations (2013)
- Tang, Min; Chen, Xia; Hu, Weidong; Yu, Wenxian: Generation of a probabilistic fuzzy rule base by learning from examples (2012) ioport
- Guillaume, Serge; Charnomordic, Brigitte: Learning interpretable fuzzy inference systems with FisPro (2011) ioport
- Alonso, José M.; Magdalena, Luis; González-Rodríguez, Gil: Looking for a good fuzzy system interpretability index: an experimental approach (2009) ioport
- Guillaume, Serge; Magdalena, Luis: Expert guided integration of induced knowledge into a fuzzy knowledge base (2006) ioport
Further publications can be found at: https://www7.inra.fr/mia/M/fispro/fispro2013_en_publications.html