Compared with conventional weighted voting methods, class-specific soft voting (CSSV) system has several advantages. On one hand, it not only deals with the soft class probability outputs but also refines the weights from classifiers to classes. On the other hand, the class-specific weights can be used to improve the combinative performance without increasing much computational load. This paper...

As real industrial processes have measurement samples with noises of different statistical characteristics and obtain the sample one by one usually, on-line sequential learning algorithms which can achieve better learning performance for systems with noises of various statistics are necessary. This paper proposes a new online Extreme Learning Machine (ELM, of Huang et al.) algorithm, namely rec...

This paper proposes an approach to the identification of evolving fuzzy Takagi–Sugeno systems based on the optimally pruned extreme learning machine (OP-ELM) methodology. First, we describe ELM, a simple yet accurate learning algorithm for training single-hidden layer feed-forward artificial neural networks with random hidden neurons. We then describe the OP-ELM methodology for building ELM mod...

Extreme learning machine (ELM) as an emergent technology has shown its good performance in regression applications as well as in large dataset (and/or multi-label) classification applications. The ELM theory shows that the hidden nodes of the ‘‘generalized’’ single-hidden layer feedforward networks (SLFNs), which need not be neuron alike, can be randomly generated and the universal classificati...

Observations of localized heat loads to ELM filament impact on the main chamber in JET are reviewed and drawn together to form a coherent picture of the exhaust phase of the ELM. Presently available JET data is quantitatively explained by a parallel loss model of ELM filament dynamics, in which the evolution of filament density and temperature proceeds via a competition of radial advection and ...

Abstract: Focusing on short-term wind power forecast, a method based on the combination of Genetic Algorithm (GA) and Extreme Learning Machine (ELM) has been proposed. Firstly, the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this, the ELM was used to establish the forecast model for short-term wind power. Then, the GA was used to opti...

Embedding cost-sensitive factors into the classifiers increases the classification stability and reduces the classification costs for classifying high-scale, redundant, and imbalanced datasets, such as the gene expression data. In this study, we extend our previous work, that is, Dissimilar ELM (D-ELM), by introducing misclassification costs into the classifier. We name the proposed algorithm a...

Extreme Learning Machine (ELM) is a neural network architecture in which hidden layer weights are randomly chosen and output layer weights determined analytically. We interpret ELM as an approximation to a network with infinite number of hidden units. The operation of the infinite network is captured by neural network kernel (NNK). We compare ELM and NNK both as part of a kernel method and in n...

A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with a low confidence coefficient tends to mislead t...

In multimodal biometric system, the effective fusion method is necessary for combining information from various modality systems. In this study a new approach to overcome the limitations by using multiple pieces of evidence of the same identity: iris and fingerprint, by combining ELM and Genetic Algorithm. According to ELM theory: “The hidden node / neuron parameters are not only independent of...