Error Minimized Extreme Learning Machine (EM-ELM) proposed by Feng et al. [1] can automatically determine the number of hidden nodes in generalized Single-hidden Layer Feedforward Networks (SLFNs). We recently found that some of the hidden nodes that are added into the network may play a very minor role in the network output, which increases the network complexity. Hence, this paper proposes an...

This paper delivers a study on the change of rank of input matrix in Extreme Learning Machine (ELM) and the relationship between the rank of input matrix and the residence error of training an ELM. From the viewpoint of data analysis, the study reveals why ELM has a decreasing residence error with the increase of number of nodes in hidden layer and what role the Sigmoid function plays in increa...

This work details development of dynamic neural models of a yeast fermentation chemical reactor using Extreme Learning Machines (ELM). The ELM approach calculates very efficiently, without nonlinear optimisation, dynamic models, but only in the non-recurrent serial-parallel configuration. It is shown that in the case of the considered benchmark the ELM technique gives models which are also quit...

In this letter, a class of improved extreme learning machines (ELM) encoding a priori information is proposed to obtain better generalization performance and much faster convergence rate for function approximation. According to Fourier series expansion theory, the hidden neurons activation functions in the improved ELM are sine and cosine functions. In addition, the improved ELM analytically de...

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...

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtai...

Effects of nitric oxide (NO)-containing compounds on opossum esophageal longitudinal smooth muscle in vitro were examined. Sodium nitroprusside (SNP) and authentic NO produced a biphasic concentration-dependent relaxation-contraction sequence in the esophageal longitudinal muscle (ELM) but only a concentration-dependent relaxation of the lower esophageal sphincter (LES) and no effect in the eso...

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming dat...

Regression is one of the most basic problems in data mining. For regression problem, extreme learning machine (ELM) can get better generalization performance at a much faster learning speed. However, the enlarging volume of datasets makes regression by ELM on very large scale datasets a challenging task. Through analyzing the mechanism of ELM algorithm, an efficient parallel ELM for regression ...

Extreme Learning Machine (ELM) is a new learning method for single-hidden layer feedforward neural network (SLFN) training. ELM approach increases the learning speed by means of randomly generating input weights and biases for hidden nodes rather than tuning network parameters, making this approach much faster than traditional gradient-based ones. However, ELM random generation may lead to nono...