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Open Source

Software Vendor Short description
FisPro INRA - Institut National de la Recherche Agronomique, Paris, France FisPro is an open source portable software for designing and handling fuzzy inference systems. It is an open evolutive development framework for fuzzy logic researchers, and a modeling and simulation tool for non specialist users. It includes well known learning methods, as well as new ones with interpretability constraints. It provides specialized graphic views, in particular an educational inference graphic tool. FisPro does not necessitate any proprietary software to run, only a java 2 virtual machine is required, and a C++ compiler on non Win32 platforms.
Kappalab Michel Grabisch, Université Paris I Panthéon-Sorbonne
Ivan Kojadinovic, University of Auckland, New Zealand
Patrick Meyer, University of Luxembourg
Kappalab, which stands for "laboratory for capacities", is a package for the GNU R statistical system. It is a toolbox for capacity (or non-additive measure, fuzzy measure) and integral manipulation on a finite setting which can be used in the framework of decision making or cooperative game theory. It is distributed as a free software under the CeCILL license which is basically a GNU GPL license compatible with French law.
GUAJE FUZZY Jose M. Alonso
European Centre for Soft Computing, Spain
GUAJE stands for Generating Understandable and Accurate fuzzy models in a Java Environment. Thus, it is a free software tool (licensed under GPL-v3) with the aim of supporting the design of interpretable and accurate fuzzy systems by means of combining several preexisting open source tools, taking profit from the main advantages of all of them. It is a user-friendly portable tool designed and developed in order to make easier knowledge extraction and representation for fuzzy systems, paying special attention to interpretability issues. GUAJE lets the user define expert variables and rules, but also provide supervised and fully automatic learning capabilities. Both types of knowledge, expert and induced, are integrated under the expert supervision, ensuring interpretability, simplicity and consistency of the knowledge base along the whole process. Notice that, GUAJE is is an upgraded version of the free software called KBCT (Knowledge Base Configuration Tool).

You can find a thorough survey on free and/or open source software for fuzzy systems at the following thematic website: http://sci2s.ugr.es/fss

 

Commercial

Software Vendor Short description
LFLC
Linguistic Fuzzy Logic Controller
Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Czech Republic Linguistic Fuzzy Logic Controller (LFLC) is in several respects different from the standard fuzzy logic controllers. It is based on the deep mathematical results in fuzzy logic, and its application in linguistics.
MLF
Machine Learning Framework for Mathematica
Uni Software Plus, Linz, Austria The machine learning framework mlf is a universal tool for all those, who want to create understandable computational models from data. It combines different optimized future looking fuzzy logic based machine learning methods and algorithms, which create understandable computational models. Fully implemented in C++, mlf is integrated into Mathematica's high level, symbolic computation, visualization and programming environment.
MARS Salford Systems, San Diego, CA, USA MARS (Multivariate Adaptive Regression Splines) is a companion to CART that focuses on the development and deployment of accurate and easy-to-understand regression models. The MARS model is designed to predict continuous numeric outcomes such the average monthly bill of a mobile phone customer or the amount that a shopper is expected to spend in a web site visit. MARS is also capable of producing high quality probability models for a yes/no outcome. A dramatic improvement over conventional stepwise and other automated regression tools, MARS performs variable selection, variable transformation, interaction detection, and self-testing, all automatically and at high speed. The MARS model is a regression but with automatically generated non-linearities and interactions included. A number of independent scientific studies have reported that MARS often outperforms neural networks in predictive accuracy while training from 100 to 1000 times faster.

 

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