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	ETcVnSta	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	HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)

Return value

HRESULT

Further information

The function F_VN_CreateStaModelExp is an expert variant of F_VN_CreateStaModel. It contains additional parameters.

Parameter

Model

The created model is returned in the interface pointer ipMlModel.

Model type

eStaType specifies whether the STA model is used for classification (TCVN_STA_CLASSIFIER), regression (TCVN_STA_REGRESSOR), clustering (TCVN_STA_CLUSTERER) or anomaly detection (TCVN_STA_NOVELTY_DETECTOR).

Vigilance parameters

This parameter is crucial for adapting the model to the respective task, as it controls the maximum size of the sub-areas to be learned.

Number of neighbors used

nNu specifies the number of neighboring sub-areas used for classification and prediction and must be >= 1.

Higher accuracy

If bDoublePrecision is TRUE, LREAL is used as the data type for the internal calculations of the model; if it is FALSE, REAL is used.

Application

For example, an STA model for classification can be created like this:

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 V3.1.4024.54 or later	PC or CX (x64) with PL50, e.g. Intel 4-core Atom CPU	Tc3_Vision