Package: fispro
Version: 3.5-1
Architecture: i386
Maintainer: bch <bch@supagro.inra.fr>
Installed-Size: 52833
Depends: libc6 (>= 2.4), libgcc1 (>= 1:3.4), libgsl2, libstdc++6 (>= 5.2), openjdk-8-jre | openjdk-7-jre
Filename: ./i386/fispro_3.5-1_i386.deb
Size: 13291564
MD5sum: 3f6953843c5ca5a1e51e478d31ddb217
SHA1: 34a6869af90e0cf386b204dc0415fc54486b166e
SHA256: c6255682759ebb4edf70d8e039d9cff3508d51f4ac93906b67ffe422ee037d68
Section: Math
Priority: optional
Description: fuzzy inference system design
 FisPro (Fuzzy Inference System Professional) allows to create fuzzy inference systems and to use them for reasoning purposes.
 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.
   FisPro implementation allows to design fuzzy systems from expert knowledge or data.
 This package provides FisPro Java interface and C++ programs.

Package: fispro
Version: 3.5-1
Architecture: amd64
Maintainer: bch <bch@supagro.inra.fr>
Installed-Size: 53346
Depends: libc6 (>= 2.14), libgcc1 (>= 1:3.4), libgsl2, libstdc++6 (>= 5.2), openjdk-8-jre | openjdk-7-jre
Filename: ./amd64/fispro_3.5-1_amd64.deb
Size: 13390468
MD5sum: 825f7110d62fdbced777711f8c40af02
SHA1: b07c53edc7146a08c26ca8ddecfc63270a74c05b
SHA256: bc004034d0d2b1d95ef2e19ba49b1e8c0ee2bf6abb95274af07d0fc19d456c1e
Section: Math
Priority: optional
Description: fuzzy inference system design
 FisPro (Fuzzy Inference System Professional) allows to create fuzzy inference systems and to use them for reasoning purposes.
 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.
   FisPro implementation allows to design fuzzy systems from expert knowledge or data.
 This package provides FisPro Java interface and C++ programs.