Package: fispro
Version: 3.5-1
Architecture: i386
Maintainer: bch <bch@supagro.inra.fr>
Installed-Size: 52853
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: 14052244
MD5sum: b2adffad903437523258c27566f24ae4
SHA1: 8d3d312e5959096fef6efe008fdc374f059d4d51
SHA256: 7e0c67d6ad6de0c93446fbaf84582d3833cf9a39cbcd21e63017a1e47b8cc17f
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: 53350
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: 14318592
MD5sum: fcf8ec7f6a429a1df8ec679c9266fbd9
SHA1: 99f509900967801b1625a700f2c99edff2871dc5
SHA256: cc1a98c63cbf97bc7118898cfc9de23453c4ab3ec57ac52af69d156d63523332
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.