F_VN_CreateStaModelExp
Create a Simplified TopoART neural network of the specified type. The initial reference count is set to one if a new model is created and kept, otherwise. Models of this type support on-line training (sample by sample), retraining, as well as scalar and vectorial predictions. It requires all input except class labels (i.e., samples and training outputs/predictions) to lie in the interval [0.0, 1.0]. The predictions of regressors need to be rescaled from the interval [0.0, 1.0] to their respective value range before usage. Depending on the parameter settings and the number of available training samples, repeated training with the same data may improve the results. Like other neural networks based on the Adaptive Resonance Theory (ART), Simplified TopoART neural networks are not prone to catastrophic inference and patricularly well-suited to incremental learning tasks. (expert function)
Syntax
Definition:
FUNCTION F_VN_CreateStaModelExp : HRESULT
VAR_INPUT
ipMlModel : Reference To ITcVnMlModel;
eStaType : ETcVnSta;
fRho : LREAL;
nNu : UDINT;
bDoublePrecision : BOOL;
hrPrev : HRESULT;
END_VAR
Inputs
|
Name |
Type |
Description |
|---|---|---|
|
ipMlModel |
Reference To ITcVnMlModel |
Returns the created model (Non-zero interface pointers are reused.) |
|
eStaType |
Simpified TopoART model type | |
|
fRho |
LREAL |
Vigilance parameter (controls the number of neurons that are inserted and the maximum size of the formed categories; valid range: [0.0, 1.0]; suggested range: [0.8, 1.0)) |
|
nNu |
UDINT |
Number of neurons used for classification and prediction (must be larger than or equal to 1; suggested range: [1, 10); default: 3) |
|
bDoublePrecision |
BOOL |
If true, the model is generated with double precision (LREAL) instead of single precision (REAL). (default: FALSE) |
|
hrPrev |
HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.) |
Return valueWeiterführende Informationen
Die Funktion F_VN_CreateStaModelExp ist eine Expert-Variante von F_VN_CreateStaModel. Sie enthält zusätzliche Parameter.
Parameter
Modell
In dem Interface Pointer ipMlModel wird das erstellte Modell zurückgegeben.
Modell-Typ
eStaType legt fest, ob das STA-Modell zur Klassifizierung (TCVN_STA_CLASSIFIER), zur Regression (TCVN_STA_REGRESSOR), zum Clustering (TCVN_STA_CLUSTERER) oder zur Anomalie-Detektion (TCVN_STA_NOVELTY_DETECTOR) genutzt wird.
Vigilanz-Parameter
Zur Anpassung des Modells an die jeweilige Aufgabenstellung ist dieser Parameter entscheidend, da er die maximale Größe der zu lernenden Teilbereiche steuert.
Anzahl genutzter Nachbarn
nNu gibt die Anzahl der für die Klassifizierung und Vorhersage verwendeten Nachbarteilbereiche an und muss >= 1 sein.
Höhere Genauigkeit
Wenn bDoublePrecision TRUE ist, wird LREAL als Datentyp für die internen Berechnungen des Modells genutzt; wenn es FALSE ist, wird REAL genutzt.
Anwendung
Ein STA-Modell zur Klassifikation kann z.B. so erstellt werden:
hr := F_VN_CreateStaModel(
ipMlModel := ipMlModel,
eStaType := TCVN_STA_CLASSIFIER,
fRho := 0.9,
nNu := 3,
bDoublePrecision := FALSE,
hrPrev := hr);
Required License
TC3 Vision Machine Learning
System Requirements
Development environment | Target platform | PLC libraries to include |
|---|---|---|
TwinCAT V3.1. 4024.44 or later | PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU | Tc3_Vision |