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...

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...

This paper discusses the performance of a novel Coral Reefs Optimization – Extreme Learning Machine (CRO–ELM) algorithm in a real problem of global solar radiation prediction. The work considers different meteorological data from the radiometric station at Murcia (southern Spain), both from measurements, radiosondes and meteorological models, and fully describes the hybrid CRO–ELM to solve the ...

Extreme learning machines (ELMs) has been theoretically and experimentally proved to achieve promising performance at a fast speed for supervised classification tasks. However, it does not perform well on imbalanced binary tasks tends get biased toward the majority class. Besides, since large amount of training data with labels are always available in real world, there is an urgent demand devel...

Recently there have been renewed interests in single-hidden-layer neural networks (SHLNNs). This is due to its powerful modeling ability as well as the existence of some efficient learning algorithms. A prominent example of such algorithms is extreme learning machine (ELM), which assigns random values to the lower-layer weights. While ELM can be trained efficiently, it requires many more hidden...

In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compa...

In this study, machine vision technology was used to separate the samples and detect rotting degrees of fresh-cut cauliflowers. First, improved watershed algorithm for segmentation cauliflower extraction single-sample. Then, three color models, a gray co-occurrence matrix two feature algorithms were extract color, texture spectral parameters images. At same time, Partial Least Squares Discrimin...

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n = 34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of trainin...

3D shape features play a crucial role in graphics applications, such as 3D shape matching, recognition, and retrieval. Various 3D shape descriptors have been developed over the last two decades; however, existing descriptors are handcrafted features that are labor-intensively designed and cannot extract discriminative information for a large set of data. In this paper, we propose a rapid 3D fea...