Fig. 2. Model training with ensemble learning.  a, Model training with M Bragg shifted wavelengths as input to N (6×4) multi-layer Perceptron regressors, which includes two hidden layers of 150 and 300 neurons, respectively. The whole surface nodes set is divided by N windows, with each window covering the same number of nodes. Each window involves an ANN-based sub-model with the same M (29) input FBGs data. b, Prediction time and accuracy as a result of changing the node density (7×5, 11×7 and 21×13 nodes) and the window size (from 1/16 to 1 times the A4 size), ), where the size ratio refers to the ratio of window size to the A4 size.