Package: fispro
Version: 3.5-1
Architecture: i386
Maintainer: bch <bch@supagro.inra.fr>
Installed-Size: 53960
Depends: libc6 (>= 2.4), libgcc1 (>= 1:3.4), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), openjdk-8-jre | openjdk-7-jre, libgsl2 | libgsl0ldbl
Filename: ./i386/fispro_3.5-1_i386.deb
Size: 14415204
MD5sum: 3b45c1913bcba8bfc7a686816fa7673d
SHA1: 1eadce6b350b7bbb0e93cee106e9dae1771a98bc
SHA256: 259bc636311b4a22599c4b663f38b3e1944066c6f2f8bbcc7b4f446a008217a9
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: 53707
Depends: libc6 (>= 2.14), libgcc1 (>= 1:3.4), libgsl23 (>= 2.5), libstdc++6 (>= 5.2), openjdk-8-jre | openjdk-7-jre, libgsl2 | libgsl0ldbl
Filename: ./amd64/fispro_3.5-1_amd64.deb
Size: 14476052
MD5sum: 06828b8f28e96c99255b8144e630361e
SHA1: c084de80ff4d8d8d49f50a41309d2038063eecf3
SHA256: c4967d92a291713879a3408cfb9dd00257b694236646b6fc0d97e846050cf999
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.