Min Gan

TL;DR: The fuzzy restricted Boltzmann machine (FRBM) and its learning algorithm are proposed in this paper, in which the parameters governing the model are replaced by fuzzy numbers, which shows that the representation capability of FRBM model is significantly better than the traditional RBM.

TL;DR: A sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm and prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

TL;DR: From the results of the experiments, it is found that: 1) the simplified Jacobian proposed by Ruano et al. is not a good choice for the VP algorithm; moreover, it may render the algorithm hard to converge; 2) the fourth algorithm perform moderately among these four algorithms; and 3) the combination of VP algorithm and Levenberg–Marquardt method is more effective than the combined algorithm and Gauss–Newton method.

TL;DR: This paper proposes to determine the regularization parameter using the weighted generalized cross-validation method at every iteration of ill-conditioned SNLLS problems based on the variable projection method to produce a consistent demand of decreasing at successive iterations.

TL;DR: A variable projection algorithm is proposed to estimate the model parameters more efficiently by eliminating the linear parameters through the orthogonal projection of RBF-ARX model by substantially reducing the dimension of parameter space.