Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Ex...

In this paper, a computationally efficient framework, referred to as ensemble of subset online sequential extreme learning machine (ESOS-ELM), is proposed for class imbalance learning from a concept-drifting data stream. The proposed framework comprises a main ensemble representing short-term memory, an information storage module representing long-term memory and a change detection mechanism to...

As a new machine learning approach, the extreme learning machine (ELM) has received much attention due to its good performance. However, when directly applied to hyperspectral image (HSI) classification, the recognition rate is low. This is because ELM does not use spatial information, which is very important for HSI classification. In view of this, this paper proposes a new framework for the s...

We consider the use of extreme learning machines (ELM) for computational partial differential equations (PDE). In ELM hidden-layer coefficients in neural network are assigned to random values generated on $[-R_m,R_m]$ and fixed, where $R_m$ is a user-provided constant, output-layer trained by linear or nonlinear least squares computation. present method computing optimal value based evolution a...

For high dimensional pattern recognition problems, the learning speed of gradient based training algorithms (back-propagation) is generally very slow. Local minimum, improper learning rate and over-fitting are some of the other issues. Extreme learning machine was proposed as a non-iterative learning algorithm for single-hidden layer feed forward neural network (SLFN) to overcome these issues. ...

This paper proposes a learning framework for single-hidden layer feedforward neural networks (SLFN) called optimized extreme learning machine (O-ELM). In O-ELM, the structure and the parameters of the SLFN are determined using an optimization method. The output weights, like in the batch ELM, are obtained by a least squares algorithm, but using Tikhonov’s regularization in order to improve the ...

In this paper, three novel classification algorithms aiming at (semi-)supervised action classification are proposed. Inspired by the effectiveness of discriminant subspace learning techniques and the fast and efficient Extreme Learning Machine (ELM) algorithm for Single-hidden Layer Feedforward Neural networks training, the ELM algorithm is extended by incorporating discrimination criteria in i...