Extreme learning machine (ELM) uses a non-iterative method to train single-hidden-layer feed-forward networks (SLFNs), which has been proven to be an efficient and effective learning model for both classification and regression. The main advantage of ELM lies in that the input weights as well as the hidden layer biases can be randomly generated, which contributes to the analytical solution of o...

Subject review The developmental tendencies of cumulative displacement time series associated with reservoir landslides influenced by large water reservoirs must be effectively predicted. However, traditional methods do not encompass the dynamic response relationships between landslide deformation and its influencing factors. Therefore, a new approach based on the exponential smoothing (ES) and...

Abstract We address a new numerical method based on class of machine learning methods, the so-called Extreme Learning Machines (ELM) with both sigmoidal and radial-basis functions, for computation steady-state solutions construction (one-dimensional) bifurcation diagrams nonlinear partial differential equations (PDEs). For our illustrations, we considered two benchmark problems, namely (a) one-...

Reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems have aroused extensive research interests due to the controllable communication environment and performance of combating multi-path interference. However, as premise RIS-assisted OFDM systems, accuracy channel estimation is severely degraded by increased possibility insufficient cyclic pr...

In this paper an improvement of the optimally pruned extreme learning machine (OP-ELM) in the form of a L2 regularization penalty applied within the OP-ELM is proposed. The OP-ELM originally proposes a wrapper methodology around the extreme learning machine (ELM) meant to reduce the sensitivity of the ELM to irrelevant variables and obtain more parsimonious models thanks to neuron pruning. The ...

Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemb...

The accurate prediction of strip crown is the precondition shape preset model in hot rolling. In this study, a new hybrid forecasting proposed combination with extreme learning machine (ELM) and industrial data. production data 1780 mm rolling are collected by on-site acquisition system to form dataset. Principal component analysis (PCA) used reduce dimension input for modeling samples. To impr...