ANFIS-training: Train ANFIS network
In anfis: Adaptive Neuro Fuzzy Inference System in R

Description Usage Arguments Value Note Author(s) See Also

ANFIS on-line or off-line hybrid Jang dynamic learning training process. In addition for off-line learning there is also adaptive learning coefficient and momentum term.

LSE(object, A, B, initialGamma = 1000)

## S4 method for signature 'ANFIS'
LSE(object, A, B, initialGamma = 1000)

trainHybridJangOffLine(object, epochs = 5, tolerance = 1e-05,
  initialGamma = 1000, k = 0.01)

## S4 method for signature 'ANFIS'
trainHybridJangOffLine(object, epochs = 5,
  tolerance = 1e-05, initialGamma = 1000, k = 0.01)

trainHybridOffLine(object, epochs = 5, tolerance = 1e-05,
  initialGamma = 1000, eta = 0.05, phi = 0.2, a = 0.01, b = 0.1,
  delta_alpha_t_1 = list())

## S4 method for signature 'ANFIS'
trainHybridOffLine(object, epochs = 5, tolerance = 1e-05,
  initialGamma = 1000, eta = 0.05, phi = 0.2, a = 0.01, b = 0.1,
  delta_alpha_t_1 = list())

trainHybridJangOnLine(object, epochs = 5, tolerance = 1e-15,
  initialGamma = 1000, k = 0.01, lamda = 0.9, S = matrix(nrow = 0, ncol
  = 0))

## S4 method for signature 'ANFIS'
trainHybridJangOnLine(object, epochs = 5,
  tolerance = 1e-15, initialGamma = 1000, k = 0.01, lamda = 0.9,
  S = matrix(nrow = 0, ncol = 0))

`object`	ANFIS' class object.
`A`	internal matrix for Iterative Least Squares Estimation of AX=B.
`B`	internal matrix for Iterative Least Squares Estimation of AX=B.
`initialGamma`	numeric large number >> 0. Default 1000.
`epochs`	the max number of training epochs. Default 5.
`tolerance`	convergence error to stop training. Default 1e-5.
`k`	numeric with the initial step size for learning rule. Default 0.01.
`eta`	numeric learning rule coefficient. Default 0.05.
`phi`	numeric momentum rule coefficient. Default 0.2.
`a`	numeric step to increase eta if delta_e is < 0, i.e. descending. Default value 0.01.
`b`	numeric fraction to decrease eta if delta_e is > 0, i.e. ascending. Default value is 0.1.
`delta_alpha_t_1`	list with numeric matrix with last time step. Default list().
`lamda`	0 < numeric < 1 forgetting factor. Default 0.9.
`S`	covariance matrix for on-line LSE. Default matrix(nrow=0, ncol=0).

`matrix`	with the system solution for LSE output.
`error`	numeric vector with training associated errors (pattern or epoch) according to trainingType.
`convergence`	TRUE/FALSE if it reached convergence or not.
`updated`	trainingType, premises, consequents, error, residuals, fitted.values and coefficient.