The limiting velocity in open channels to prevent long-term sedimentation is predicted in this paper using a powerful soft computing technique known as Extreme Learning Machines (ELM). The ELM is a single Layer Feed-forward Neural Network (SLFNN) with a high level of training speed. The dimensionless parameter of limiting velocity which is known as the densimetric Froude number (Fr) is predicte...

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-...

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 ...

—Extreme learning machine (ELM) is an extremely fast learning method and has a powerful performance for pattern recognition tasks proven by enormous researches and engineers. However, its good generalization ability is built on large numbers of hidden neurons, which is not beneficial to real time response in the test process. In this paper, we proposed new ways, named " constrained extreme lear...

The extreme learning machines (ELMs) have been proposed for generalized single-hidden-layer feedforward networks (SLFNs) which need not be neuron alike and perform well in both regression and classification applications. An active topic in ELMs is how to automatically determine network architectures for given applications. In this paper, we propose an extreme learning machine with adaptive grow...

This paper presents a new method to classify and identify high impedance faults in radial distribution system. The proposed methodology uses extreme learning machine (ELM) as a classifier for identifying the high impedance arc-type faults. The network is learned by data from simulation of a simple radial system under different fault and system conditions. Magnitudes of third and fifth harmonic ...

Network packet transport services (namely the Internet) are subject to significant security issues. This paper aims to apply Machine Learning methods based on Neural Networks (Extreme Learning Machines or ELM) to analyze the Internet traffic in order to detect specific malicious activities. This is performed by classifying traffic for a key service run over the internet: the Domain Name System ...

Computational intelligence techniques especially neural networks have been attracting a large number of researchers' attentions in the past three decades. It has been well known that conventional learning methods on neural networks have apparent drawbacks and limitations including: (1) slow learning speed, (2) trivial human tuned parameters, and (3) complicated learning algorithms. Extreme Lear...

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...