The role of this function is to update parameters in the ANFIS method.
This function is called by the main function of the ANFIS method,
ANFIS.update(data.train, def, rule.data.num, miu.rule, func.tsk, varinp.mf, step.size = 0.01)
a matrix (\(m \times n\)) of normalized data for the training process, where \(m\) is the number of instances and \(n\) is the number of variables; the last column is the output variable.
a predicted value
a matrix containing the rule base in integer form.
a matrix with the degrees of rules. See
a matrix of parameters of the function on the consequent part using the Takagi Sugeno Kang model.
a matrix of parameters of membership functions of the input variables.
a real number between 0 and 1 representing the step size of the gradient descent.