We present a closed form expression for initializing the input weights in a multilayer perceptron, which can be used as the first step in synthesis of an Extreme Learning Machine. The expression is based on the standard function for a separating hyperplane as computed in multilayer perceptrons and linear Support Vector Machines; that is, as a linear combination of input data samples. In the abs...

Testing the correct model specification hypothesis for artificial neural network (ANN) models of the conditional mean is not standard. The traditional Wald, Lagrange multiplier, and quasi-likelihood ratio statistics weakly converge to functions of Gaussian processes, rather than to convenient chi-squared distributions. Also, their large sample null distributions are problem dependent, limiting ...

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical online applications, the sequentially coming dat...

Extreme learning machine (ELM), used for the “generalized” single-hidden-layer feedforward networks (SLFNs), is a unified learning platform that can use a widespread type of feature mappings. In theory, ELM can approximate any target continuous function and classify any disjoint regions; in application, many experiment results have already demonstrated the good performance of ELM. In view of th...

The Extreme Learning Machine (ELM) is a single-hidden layer feedforward neural network (SLFN) learning algorithm that can learn effectively and quickly. The ELM training phase assigns the input weights and bias randomly and do not change them in the whole process. Although the network works well, the random weights in the input layer can make the algorithm less effective and impact on its perfo...

In a moment when the study of outlier robustness within Extreme Learning Machine is still in its infancy, we propose a method that combines maximization of the hidden layer’s information transmission, through Batch Intrinsic Plasticity (BIP), with robust estimation of the output weights. This method named R-ELM/BIP generates a reliable solution in the presence of corrupted data with a good gene...

Especially in the Big Data era, the usage of different classification methods is increasing day by day. The success of these classification methods depends on the effectiveness of learning methods. Extreme learning machine (ELM) classification algorithm is a relatively new learning method built on feed-forward neural-network. ELM classification algorithm is a simple and fast method that can cre...

Reliable measurements of effluent quality are important for different operational tasks such as process monitoring, online simulation, and advanced control in the wastewater treatment process (WWTP). A kernel principal component analysis (KPCA) and extreme learning machine (ELM) based ensemble soft sensing model for effluent quality prediction was proposed. KPCA was used to extract nonlinear fe...

An important aspect of oil industry is rate of penetration (ROP) prediction. Many studies have been implemented to predict it. Mainly, multiple regression and artificial neural network models were used. In this paper, the objective is to compare the traditional multiple regression with two artificial intelligence techniques; extreme learning machines (ELM) and radial basis function networks (RB...

In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a lowdimensional space where classification is performed by a linear classifier, we e...