BACKGROUND Elastin is a signature protein of the arteries and lungs, thus it was hypothesized that elastin is subject to enzymatic degradation during cardiovascular and pulmonary diseases. The aim was to investigate if different fragments of the same protein entail different information associated to two different diseases and if these fragments have the potential of being diagnostic biomarkers...

The primitive Extreme Learning Machine (ELM) [1, 2, 3] with additive neurons and RBF kernels was implemented in batch mode. In this paper, its sequential modification based on recursive least-squares (RLS) algorithm, which referred as Online Sequential Extreme Learning Machine (OS-ELM), is introduced. Based on OS-ELM, Online Sequential Fuzzy Extreme Learning Machine (Fuzzy-ELM) is also introduc...

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational ...

The extreme learning machine (ELM) that is proposed by Huang is designed based on single-hidden layer feedforward neural networks (SLFNs), which can randomly choose the parameters of hidden nodes and the output weights gotten analytically. So it can get the solution fastly. However, the learning time of ELM is mainly spent on calculating the Moore-Penrose generalized inverse matrices of the hid...

Extreme learning machine (ELM) as a neural network algorithm has shown its good performance, such as fast speed, simple structure etc, but also, weak robustness is an unavoidable defect in original ELM for blended data. We present a new machine learning framework called “LARSEN-ELM” for overcoming this problem. In our paper, we would like to show two key steps in LARSEN-ELM. In the first step, ...

Extreme Learning Machine (ELM) has salient features such as fast learning speed and excellent generalization performance. However, a single extreme learning machine is unstable in data classification. To overcome this drawback, more and more researchers consider using ensemble of ELMs. This paper proposes a method integrating voting-based extreme learning machines (V-ELM) with dissimilarity (D-...

Density profiles in pedestal region (H-mode) are measured in HL-2A and the characteristics of the density pedestal are described. Cold particle deposition by Supersonic Molecular Beam Injection (SMBI) within the pedestal is verified. ELM mitigation by SMBI into the H-mode pedestal is demonstrated and the relevant physics is elucidated. The sensitivity of the effect to SMBI pressure and duration...

This paper proposes a new structure of wavelet extreme learning machine i.e. an adaptive wavelet extreme learning machine (AW-ELM) for finger motion recognition using only two EMG channels. The adaptation mechanism is performed by adjusting the wavelet shape based on the input information. The performance of the proposed method is compared to ELM using wavelet (W-ELM0 and sigmoid (Sig-ELM) acti...