API Reference FLex v0.2.2

A toolkit for fuzzy logic, this library includes functions for creating fuzzy variables, sets, rules and a FLS.

An adaptive network-based fuzzy inference system (ANFIS) is a kind of artificial neural network that is based on Takagi–Sugeno fuzzy inference system, this implementation use backpropagation, only Gaussian Membership function are allowed. Reference: https://upcommons.upc.edu/bitstream/handle/2099.1/20296/Annex%201%20-%20Introduction%20to%20Adaptive%20Neuro-Fuzzy%20Inference%20Systems%20%28ANFIS%29.pdf Jang, J-SR. "ANFIS: adaptive-network-based fuzzy inference system." IEEE transactions on systems, man, and cybernetics 23.3 (1993): 665-685.

Mamdani fuzzy inference was first introduced as a method to create a control system by synthesizing a set of linguistic control rules obtained from experienced human operators. In a Mamdani system, the output of each rule is a fuzzy set. Since Mamdani systems have more intuitive and easier to understand rule bases, they are well-suited to expert system applications where the rules are created from human expert knowledge, such as medical diagnostics.

Takagi-Sugeno-Kang fuzzy inference uses singleton output membership functions that are either constant or a linear function of the input values. The defuzzification process for a Sugeno system is more computationally efficient compared to that of a Mamdani system, since it uses a weighted average or weighted sum of a few data points rather than compute a centroid of a two-dimensional area.

An interface to create Membership Functions reference.

An interface to create Linguistic Rules.

An interface to create Fuzzy Sets struct.

An interface to create a Fuzzy Logic Control System (FLS).

Module with helper functions.

An interface to create Fuzzy Variables.