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 this paper, a new sequential learning algorithm is constructed by combining the Online Sequential Extreme Learning Machine (OS-ELM) and Kalman filter regression. The Kalman Online Sequential Extreme Learning Machine (KOSELM) handles the problem of multicollinearity of the OS-ELM, which can generate poor predictions and unstable models. The KOSELM learns the training data one-by-one or chunk-...

Extreme Learning Machine (ELM) and its variants have been widely used for many applications due to its fast convergence and good generalization performance. Though the distributed ELM based on MapReduce framework can handle very large scale training dataset in big data applications, how to cope with its rapidly updating is still a challenging task. Therefore, in this paper, a novel Elastic Extr...

In this paper, a robust online sequential extreme learning machine (ROS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm opt imization selective ensemble (PSOSEN) is proposed. Noting that PSOSEN is a general selective ensembl...

A response surface method for reliability analysis based on iteratively-reweighted-least-square extreme learning machines (IRLS-ELM) is explored in this paper, which, highly nonlinear implicit performance functions of structures are approximated by the IRLS-ELM. Monte Carlo simulation then carried out approximate IRLS-ELM structural analysis. Some numerical examples given to illustrate proposed...

In this paper,we propose a new approach for writer identification of Chinese handwritten.In our method, we deal with writer identification of Chinese handwritten using Chinese character structure features(CSF) and extreme learning machine(ELM).To extract the features embedded in Chinese handwriting characters, special structures have been explored according to the trait of Chinese handwriting c...

In this paper, a new hidden layer construction method for Extreme Learning Machines (ELMs) is investigated, aimed at generating a diverse set of weights. The paper proposes two new ELM variants: Binary ELM, with a weight initialization scheme based on f0;1g–weights; and Ternary ELM, with a weight initialization scheme based on f 1;0;1g–weights. The motivation behind this approach is that these ...

Reliable and quick response fault diagnosis is crucial for the wind turbine generator system (WTGS) to avoid unplanned interruption and to reduce the maintenance cost. However, the conditional data generated from WTGS operating in a tough environment is always dynamical and high-dimensional. To address these challenges, we propose a new fault diagnosis scheme which is composed of multiple extre...

An extreme learning machine (ELM) is a recently proposed learning algorithm for a single-layer feed forward neural network. In this paper we studied the ensemble of ELM by using a bagging algorithm for facial expression recognition (FER). Facial expression analysis is widely used in the behavior interpretation of emotions, for cognitive science, and social interactions. This paper presents a me...

An epileptic seizure is a transient event of symptoms due to abnormal neuronal action in the brain. Electroencephalography (EEG) is the neuro physiological measurement of electrical activity in the brain as recorded by electrodes placed in the cerebral cortex. An epilepsy EEG is based on three approaches. First, a scaling and wavelet function of the Multi Wavelet Transform (MWT) offers orthogon...