GNU Fuzzy

Nauck, Detlef

doi:10.1109/fuzzy.2007.4295506

Cited by 12 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A comprehensive review of fuzzy software, an interesting discussion of useful features, and a call for building a fuzzy tool kit that supports the take-up of fuzzy systems in business applications can be found in [29], which appeared in the proceedings. During the same conference, some advanced software projects were presented, such as FrlDA [7], a free intelligent data analysis toolbox, or Xfuzzy [5].…”

Section: Fispromentioning

confidence: 99%

Learning interpretable fuzzy inference systems with FisPro

Guillaume¹,

Charnomordic²

2011

Information Sciences

124

View full text Add to dashboard Cite

a b s t r a c tFuzzy inference systems (FIS) are likely to play a significant part in system modeling, provided that they remain interpretable following learning from data. The aim of this paper is to set up some guidelines for interpretable FIS learning, based on practical experience with fuzzy modeling in various fields. An open source software system called FisPro has been specifically designed to provide generic tools for interpretable FIS design and learning. It can then be extended with the addition of new contributions. This work presents a global approach to design data-driven FIS that satisfy certain interpretability and accuracy criteria. It includes fuzzy partition generation, rule learning, input space reduction and rule base simplification. The FisPro implementation is discussed and illustrated through several detailed case studies.

show abstract

Section: Fispromentioning

confidence: 99%

Learning interpretable fuzzy inference systems with FisPro

Guillaume¹,

Charnomordic²

2011

Information Sciences

124

View full text Add to dashboard Cite

show abstract

“…HE fuzzy system research community is becoming more and more aware of the necessity of providing freely available well-assessed fuzzy technologies, in order to allow for a wider and wider adoption of these tools even by non experts of the field [1]. Since Matlab is a very common, though not free, programming environment, it is of interest to allow Matlab's users to develop fuzzy rule-based classifiers (FRBCs).…”

Section: Introductionmentioning

confidence: 99%

Providing PRTools with fuzzy rule-based classifiers

Cococcioni¹,

D’Andrea²,

Lazzerini³

2010

International Conference on Fuzzy Systems

View full text Add to dashboard Cite

This paper first reviews the state-of-the-art of fuzzy rule-based classifiers (FRBCs), then it discusses how to implement an FRBC under the Pattern Recognition Toolbox (PRTools), the de-facto standard toolbox for classification in Matlab. Such an implementation, called frbc, allows for a straightforward comparison of frbc with other classifiers already available under the PRTools. Furthermore, frbc can easily be used and combined with any other general-purpose function already available in PRTools. In this way, e.g., it becomes really easy to perform many types of feature selection, based on the accuracy achieved by frbc on the subset of features at hand. Another useful feature is the capability to export each FRBC generated by frbc as a standard Fuzzy Inference System (FIS) structure used within the Matlab Fuzzy Logic Toolbox (FLT): this allows comparisons/validations, visual inspection of the rule base, etc. In the experimental part we first assess the correctness of the implementation, by reproducing results existing in the literature. Then we show some examples of usage of frbc, combined with existing PRTools functions

show abstract

“…It is freely available and based upon open source toolboxes as well as on the authors' experience in soft computing software, through the former development of FisPro a , that offers a high level of semantics and human-machine interaction. It could be part, as a spatial package, of a wider project like the GNU Fuzzy one proposed in the 2007 Fuzz'Ieee Conference 8 .…”

Section: Introductionmentioning

confidence: 99%

Soft computing-based decision support tools for spatial data

Guillaume

Charnomordic²,

Tisseyre

et al. 2013

IJCIS

View full text Add to dashboard Cite

In many fields, due to the increasing number of automatic sensors and devices, there is an emerging need to integrate georeferenced and temporal data into decision support tools. Geographic Information Systems (GIS) and Geostatistics lack some functionalities for modelling and reasoning using georeferenced data. Soft computing techniques and software suited to these needs may be useful to implement new functionalities and use them for modelling and decision making. This work presents an open source framework designed for that purpose. It is based upon open source toolboxes, and its design is inspired by the fuzzy software capabilities developed in FisPro for ordinary non-georeferenced data. Two real world applications in Agronomy are included, and some perspectives are given to meet the challenge of using soft computing for georeferenced data.

show abstract

GNU Fuzzy

Cited by 12 publications

References 9 publications

Learning interpretable fuzzy inference systems with FisPro

Learning interpretable fuzzy inference systems with FisPro

Providing PRTools with fuzzy rule-based classifiers

Soft computing-based decision support tools for spatial data

Contact Info

Product

Resources

About