

Application programming interface.
This document contains a programmer's guide to the fuzzy machine learning
system. It provides a description of the Ada packages comprising the machine
learning system core and examples of use. If you want to use the framework
programmatically, as a library, or extend it, you have to read this document. 


Fuzzy control language. This document
contains the specification of the intuitionisitc extension of the Fuzzy
Control Language used by the fuzzy machine learning system. The extension is
compatible with the draft IEC 1131 with the following goals in mind:
 Full support of intuitionistic fuzzy sets as an extension of plain fuzzy
sets on the case of incomplete and contradictory data. Intuitionistic fuzzy
sets natively express the measurement errors as found in physical systems;
 Integrated support of dimensioned values in SI (Le
Système International d'Unités) unit system. Support for
irregular dimension units and ones outside SI;
 The language should represent a fully functional consistent subset of
FCL as specified in the draft.



Graphical
user interface.
The graphical user interface is a GTK+ front end to the fuzzy machine learning
system, which can be used to conduct experiments without programming. This
document is a user guide to the interface. 


On
intuitionistic fuzzy machine learning. The paper that laid the foundations of the
intuitionistic machine learning used in the fuzzy machine learning
system.
Abstract: The aim of this work is to propose an intuitionistic
formulation of the machine learning problem, completely independent from
probability theory. Intuitionistic fuzzy sets naturally appear in machine
learning based on possibility theory as a result of uncertainty in the
measuring process. Considering this only type of uncertainty, a notion of
fuzzy feature as a mapping. can be defined. Fuzzy features originate four
different pattern spaces. Fuzzy classification is defined as a pair of
images in the pattern spaces. Properties of the fuzzy pattern spaces
required for fuzzy inference are demonstrated.
