In big data era, the data continuously generated and its distribution may keep changes overtime. These challenges in online stream of data are known as concept drift. In this paper, we proposed the Adaptive Convolutional ELM method (ACNNELM) as enhancement of Convolutional Neural Network (CNN) with a hybrid Extreme Learning Machine (ELM) model plus adaptive capability. This method is aimed for ...

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-...

a r t i c l e i n f o The key point in multivariate calibration is to build an accurate regression relationship between the predictors and responses. In this paper, we first use extreme learning machine (ELM) to build spectroscopy regression model. Then, we propose a combinational ELM (CELM) method in which the decision function is represented as a sum of a linear hidden-node output function (a...

Evolving models have found applications in many real world systems. In this paper, potentials of the Evolving Linear Models (ELMs) in tracking control design for nonlinear variable structure systems are introduced. At first, an ELM is introduced as a dynamic single input, single output (SISO) linear model whose parameters as well as dynamic orders of input and output signals can change through ...

Extreme learning machine (ELM) is of great interest to the machine learning society due to its extremely simple training step. Its performance sensitivity to the number of hidden neurons is studied under the context of hyperspectral remote sensing image classification. An empirical linear relationship between the number of training samples and the number of hidden neurons is proposed. Such a re...

The eukaryotic linear motif (ELM http://elm.eu.org) resource is a hub for collecting, classifying and curating information about short linear motifs (SLiMs). For >10 years, this resource has provided the scientific community with a freely accessible guide to the biology and function of linear motifs. The current version of ELM contains ∼200 different motif classes with over 2400 experimentally ...

In extreme learning machines (ELM) the hidden-layer coefficients are randomly set and fixed, while output-layer of neural network computed by a least squares method. The randomly-assigned in ELM known to influence its performance accuracy significantly. this paper we present modified batch intrinsic plasticity (modBIP) method for pre-training random networks. current is devised based on same pr...

In recent years, the interest in the study of outlier robustness properties in Extreme Learning Machines (ELM) has grown. Most of the published works uses a more robust estimation method than the commonly adopted ordinary least squares. Nevertheless, the ELM network offers other challenges that also influence its robustness properties, such as the number of hidden neurons and the computational ...

This paper presents an Extreme Learning Machine (ELM) approach for a fast and accurate estimation of the power system loading margin for multiple contingencies with reduced input attributes. Active and reactive power flows of all load buses are chosen as the input features to the ELM. The training data for the ELM model are generated by using the Continuation Power Flow (CPF) method. The propos...

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected o...