Randomized algorithms for nonlinear system identification with deep learning modification - de la Rosa, Erick; Yu, Wen (Information sciences, 2016)
Both randomized algorithms and deep learning techniques have been successfully used for regression and classification problems. However, the random hidden weights of the randomized algorithms require suitable distributions in advance, and the deep learning methods do not use the output information in system identification. In this paper, the distributions of the hidden weights are obtained by the restricted Boltzmann machines. This deep learning method uses input data to construct the statistical features of the hidden weights. The output weights of the neural model are trained by normal randomized algorithms. So we successfully combine the unsupervised training (deep learning) and the supervised learning method (randomized algorithm), and take advantages from both of them. The proposed randomized algorithms with deep learning modification are validated with three benchmark problems.