Anfis-package: Adaptive Neuro Fuzzy Inference System in R

In anfis: Adaptive Neuro Fuzzy Inference System in R

Description

The package implements ANFIS Type 3 Takagi and Sugeno's fuzzy if-then rule network. This package includes the new following features:

1. Membership Functions (MF) flexible framework:

   • Flexible user-defined membership functions(MF) extensible class.

   • Independent number of (MF) for each input.

   • Different MF types, if required, for each input.

2. Type 3 Takagi and Sugeno's fuzzy if-then rule

3. Full Rule combinations, e.g. 2 inputs 2 membership functions this means that 4 fuzzy rules will be created.

4. Different learning strategies:

   trainHybridJangOffLine

   Hybrid learning, i.e. Descent Gradient for precedents and Least Squares Estimation for consequents.

   trainHybridJangOnLine

   on-line version with hybrid learning.

   trainHybridOffLine

   Adaptive learning coefficient and momentum term.

5. Multiple outputs support, i.e., the same input partition can be used to predict more than one output variable.

Author(s)

Cristobal Fresno [e-mail], Andrea S. Llera [e-mail] and Elmer A. Fernandez [e-mail]

References

1. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions on, 23(3), 665-685.

anfis documentation built on May 2, 2019, 2:38 a.m.