Anfis-package

The package implements ANFIS Type 3 Takagi and Sugeno's fuzzy if-then rule
network. This package includes the new following features:
<ol>
<li> Membership Functions (MF) flexible framework:
 <ul>
<li> Flexible user-defined membership functions(MF) extensible class.
 </li>
<li> Independent number of (MF) for each input.
 </li>
<li> Different MF types, if required, for each input.
 </li>
</ul> </li>
<li> Type 3 Takagi and Sugeno's fuzzy if-then rule
 </li>
<li> Full Rule combinations, e.g. 2 inputs 2 membership functions this
 means that 4 fuzzy rules will be created.
 </li>
<li> Different learning strategies:
 <dl>
 <dt>trainHybridJangOffLine</dt><dd>Hybrid learning, i.e. Descent Gradient
 for precedents and Least Squares Estimation for consequents.</dd> <dt>trainHybridJangOnLine</dt><dd>on-line version with hybrid learning.</dd> <dt>trainHybridOffLine</dt><dd>Adaptive learning coefficient and momentum
 term.</dd> </dl> </li>
<li> Multiple outputs support, i.e., the same input partition can be used
 to predict more than one output variable.
</li>
</ol>

ANFIS

fuzzy

membership

The package implements ANFIS Type 3 Takagi and Sugeno's fuzzy
if-then rule network with the following features: (1) Independent number of
membership functions(MF) for each input, and also different MF extensible
types. (2) Type 3 Takagi and Sugeno's fuzzy if-then rule (3) Full Rule
combinations, e.g. 2 inputs 2 membership funtions -> 4 fuzzy rules (4)
Hibrid learning, i.e. Descent Gradient for precedents and Least Squares
Estimation for consequents (5) Multiple outputs.

Anfis-package: Adaptive Neuro Fuzzy Inference System in R

Description

Arguments

References