Use Of Extreme Learning Machine Research Articles

Application of extreme learning machine and neural networks in total organic carbon content prediction in organic shale with wire line logs

Total organic carbon (TOC) is a critical parameter for source rock characterization in shale gas reservoirs. In this work, the use of extreme learning machines (ELM) for predicting TOC from well logs data have been investigated. We use log data from two wells located in an unconventional shale gas reservoir in the Sichuan Basin, China. Seven wireline logs from this well and a total of 185 TOC observations from core measurements were incorporated. Prediction accuracy of the model has been evaluated and compared with commonly used artificial neural network which is based on Levenberg-Marquardt logarithm (ANN-LM). An Extreme Learning Machine (ELM) network is a single hidden-layer feed-forward network with many advantages over multi-layer networks, such as fast computing speed and better generalization performance. The results demonstrated that TOC prediction by the ELM model and the ANN model, but the ELM method can achieve high accuracy while maintains high running speed. This study shows that ELM technology is a promising tool for TOC prediction, and this work can be incorporated into a software system that can be used in quick ‘sweet spot’ determination and well completion guidance.

Root cause analysis of process fault conditions on an industrial concentrator circuit by use of causality maps and extreme learning machines

Abstract Model-based fault diagnosis tends to be too expensive or time-consuming to apply in the mineral processing industries, owing to the complexity and variability of operations. In contrast, data-based methods are inexpensive, but do not exploit the availability of first principle knowledge of plant operations. In this investigation, the use of process causality maps in conjunction with data-based fault diagnosis is considered as a hybrid methodology that can leverage the advantages of both approaches. Extreme learning machine algorithms are used to implement the data-based component of the approach. These algorithms can be deployed rapidly on large-scale systems and have the ability to deal with highly nonlinear systems. Two different variants are considered, viz. one used in combination with principal component analysis, as well as one with a bagging algorithm for fault diagnosis and applied to an industrial concentrator circuit in South Africa. The use of process causality maps led to significantly more effective fault diagnosis, while the use of extreme learning machines in combination with principal component analysis likewise allowed markedly better fault detection and diagnosis. In contrast, fault diagnosis with the bagging approach did not perform particularly well, owing to the high degree of correlation between the variables, which made it difficult to isolate individual causal variables.

Is extreme learning machine feasible? A theoretical assessment (part II).

An extreme learning machine (ELM) can be regarded as a two-stage feed-forward neural network (FNN) learning system that randomly assigns the connections with and within hidden neurons in the first stage and tunes the connections with output neurons in the second stage. Therefore, ELM training is essentially a linear learning problem, which significantly reduces the computational burden. Numerous applications show that such a computation burden reduction does not degrade the generalization capability. It has, however, been open that whether this is true in theory. The aim of this paper is to study the theoretical feasibility of ELM by analyzing the pros and cons of ELM. In the previous part of this topic, we pointed out that via appropriately selected activation functions, ELM does not degrade the generalization capability in the sense of expectation. In this paper, we launch the study in a different direction and show that the randomness of ELM also leads to certain negative consequences. On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning. On the other hand, we theoretically justify that there also exist activation functions such that the corresponding ELM degrades the generalization capability. In particular, we prove that the generalization capability of ELM with Gaussian kernel is essentially worse than that of FNN with Gaussian kernel. To facilitate the use of ELM, we also provide a remedy to such a degradation. We find that the well-developed coefficient regularization technique can essentially improve the generalization capability. The obtained results reveal the essential characteristic of ELM in a certain sense and give theoretical guidance concerning how to use ELM.

Exploring ELM-based spatial–spectral classification of hyperspectral images

Among the different computational intelligence techniques avalaible for hyperspectral data classification, support vector machines (SVMs) have played a dominant role. Recently, a new learning algorithm for single-layer feedforward neural networks called the extreme learning machine (ELM) was proposed. This technique is competitive with SVMs in terms of accuracy, learning speed, and computational scalability. In this article, we propose and evaluate the use of ELM for land-cover classification from hyperspectral images. In addition, we consider two ELM-based techniques integrating spectral and spatial information of the image. The first is a scheme that uses a majority vote approach in order to combine the results of a pixel-wise spectral classification by ELM and a segmentation map obtained by a watershed algorithm. The second introduces spatial information from a small spatial neighbourhood after the classification by ELM. We show the usefulness of spatial–spectral ELM-based classification techniques in hyperspectral imaging. The results are compared to those obtained by similar SVM-based techniques and show improved classification results and much lower execution time. These simple and computationally cheap methods can be combined with others traditionally applied to hyperspectral images.

Question Classification Using Extreme Learning Machine on Semantic Features

In statistical machine learning approaches for question classification, efforts based on lexical feature space require high computation power and complex data structures. This is due to the large number of unique words (or high dimensionality). Choosing semantic features instead could significantly reduce the dimensionality of the feature space. This article describes the use of Extreme Learning Machine (ELM) for question classification based on semantic features to improve both the training and testing speeds compared to the benchmark Support Vector Machine (SVM) classifier. Improvements have also been made to the head word extraction and word sense disambiguation processes. These have resulted in a higher accuracy (an increase of 0.2%) for the classification of coarse classes compared to the benchmark. For the fine classes, however, there is a 1.0% decrease in accuracy but is compensated by a significant increase in speed (92.1% on average).