anfis (version 0.99.1)

LSE: Train ANFIS network

Description

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.

Usage

LSE(object, A, B, initialGamma = 1000)
"LSE"(object, A, B, initialGamma = 1000)
trainHybridJangOffLine(object, epochs = 5, tolerance = 1e-05, initialGamma = 1000, k = 0.01)
"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())
"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))
"trainHybridJangOnLine"(object, epochs = 5, tolerance = 1e-15, initialGamma = 1000, k = 0.01, lamda = 0.9, S = matrix(nrow = 0, ncol = 0))

Arguments

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).

Value

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.

See Also

ANFIS-class

Other ANFIS: ANFIS-class; anfis3; coef, coef,ANFIS-method, coefficients, coefficients,ANFIS-method, fitted, fitted,ANFIS-method, fitted.values, fitted.values,ANFIS-method, resid, resid,ANFIS-method, residuals, residuals,ANFIS-method, summary, summary,ANFIS-method; getConsequents, getConsequents, getConsequents,ANFIS-method, getConsequents,ANFIS-method, getErrors, getErrors, getErrors,ANFIS-method, getErrors,ANFIS-method, getPremises, getPremises, getPremises,ANFIS-method, getPremises-methods, getRules, getRules, getRules,ANFIS-method, getRules-methods, getTrainingType, getTrainingType, getTrainingType,ANFIS-method, getTrainingType,ANFIS-method; initialize, initialize,ANFIS-method; plotMF, plotMF, plotMF,ANFIS-method, plotMF-methods, plotMFs, plotMFs, plotMFs,ANFIS-method, plotMFs-methods; plot, plot,ANFIS-method; predict, predict,ANFIS-method; print, print,ANFIS-method, show, show,ANFIS-method; trainSet