CreateStaModel

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. (additional expert function providing parameters for fine-tuning and noise reduction)

Syntax

Definition:

HRESULT CreateStaModel(
    HRESULT        hrPrev,
    ITcVnMlModel*& ipMlModel,
    ETcVnSta       eStaType,
    double         fRho,
    ULONG          nNu = 3,
    bool           bDoublePrecision = false,
    double         fBetaSbm = 0.0,
    ULONG          nPhi = 1,
    ULONG          nTau = 100
)

Parameters

Name	Type	Default	Description
hrPrev	HRESULT		HRESULT indicating the result of previous operations (If SUCCEEDED(hrPrev) equals false, no operation is executed.)
ipMlModel	ITcVnMlModel*&		Returns the created model (Non-zero interface pointers are reused.)
eStaType	ETcVnSta		Simpified TopoART model type
fRho	double		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	ULONG	3	Number of neurons used for classification and prediction (must be larger than or equal to 1; suggested range: [1, 10); default: 3)
bDoublePrecision	bool	false	If true, the model is generated with double precision (LREAL) instead of single precision (REAL). (default: FALSE)
fBetaSbm	double	0.0	Learning rate of the second best-matching neuron (learning the second best-matching neuron keeps related categories closer together and might improve the results but may require a higher number of neurons and training runs; a value of 0.0 disables learning of the second best-matching neuron; valid range: [0.0, 1.0]; suggested range: [0.0, 0.5]); default: 0.0
nPhi	ULONG	1	Number of samples a neuron must have learnt to become permanent (required for noise reduction; works in conjunction with nTau; must be larger than or equal to 1; higher values intensify noise reduction; a value of 1 disables removal of neuron candidates; default: 1)
nTau	ULONG	100	Number of learning steps after which node removal is performed (required for noise reduction; works in conjunction with nPhi; must be large enough to allow neuron candidates representing non-noise samples to become the best-matching neuron at least nPhi times before node removal; default: 100)

Return value

HRESULT

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