Built-in Fuzzy Sets

Home
Software
Prices
Download
Yahoo Charts

NEW:
ADVANCED
TRADESTATION TECHNIQUE

US Markets Daily
snapshots

Technical
Description

Forthcoming Releases:

Fuzzy Logic:
sFLC3
DLL & API

Neural Net:
Release of NXL3
DLL & API

Built-in Fuzzy Sets

Fuzzy sets are one of the 3 components of our FL controllers, along with the Variable Set and the Rule Set. The controller being constructed with a given shape (3x3 or 5x5), fuzzy sets will obviously have to conform the type of FL controller being built.

Again, in order to keep sFLC3 simple to use, a number of predefined fuzzy sets have been built in and can therefore be called and modified with just one or 2 API calls (see sample code for more details).

Lastly, the semantics behind variables has not been ignored and has also been kept simple by attaching a given set of "labels" to each of our fuzzy states. Labels can be changed like anything else.

const string strStates5[3][5] =

{
"Very Low", "Low", "Med", "High", "Very High",
"Very Neg.", "Neg.", "Null", "Pos.", "Very Pos.",
"Very Small", "Small", "Avg.", "Big", "Very Big"
};

const string strStates3[3][3] =

{
"Low", "Med", "High",
"Neg.", "Null", "Pos.",
"Small", "Avg.", "Big"
};

"3x3 Shape"

The 3x3 Shape means that the 2 variables will both use a 3-state fuzzy set (such as "Low", "Medium", and "High"). The sFLC3 API includes 9 predefined fuzzy set models to facilitate the building of fuzzy logic controllers:

"Regular",
"Reg-NarrowCentre",
"Reg-Wide",
"Reg-Squash",
"Reg-Separated",
"Balanced",
"Bal-Separated",
"Bal-Centre",
"Bal-VerySeparated"

To better comprehend what is behind those names, a spreadsheet here describes and displays the different shapes: FS3.XLS or FS3.MHT

Nota Bene 1: All fuzzy sets are normalised to the [-1:+1] range, meaning -1 is the 'absolute' definition of "Very Low" or "Very Small", and +1 is obviously the opposite "Very High" or "Very Big". Secondly, a variable associated to a fuzzy set will be given a 'degree of membership' for each Fuzzy State in the associated Fuzzy Set. That degree of memebership is measured from 0 to 1 or 0% (not attributable to that state) to 100% (fully attributable to that state).

Nota Bene 2: Users are limited to predefined Fuzzy Set Models and can also create their own custom fuzzy sets.