Research Article

GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM

Algorithm 2

Ensemble RELM (ERELM) based AdaBoost-R.T.
Input: Sequence of samples
       where output
      
Output: Final hypothesis .
(1)    Initialize:
          Machine number or iteration
          Distribution for all
          Weak learning algorithm
            Maximum number of iterations
           (machines) .
      Threshold for demarcating correct and
      Incorrect predictionst
(2)   while    do
(3)       Call weak learner (i.e., Algorithm 1),
       providing it with distribution
(4)       Build the regression model:
(5)       Compute absolute relative error for each
      training sample as
                         () 
(6)       Compute the error rate of as follows:
                     ()
(7)       Set
(8)       Update distribution as follows:
            ( )
(9)       Compute contribution of to the
      final result:
(10)     Normalize such that .
(11)      Set .
(12) end while