Research Article
GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
Input: Samples , number of hidden | node | Output: | (1) Step 1. Randomly generate the input | weights and the bias value | (2) Step 2. Calculate the hidden output matrix | using (28)–(30) | (3) Step 3. Calculate the output weight value , | | where is the Moore-Penrose generalized | inverse of matrix and . | (4) Step 4. Apply regularization of ELM using | (31)–(34). |
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