Lithofacies Recognition Based on Extreme Learning Machine

2012 ◽  
Vol 241-244 ◽  
pp. 1762-1767 ◽  
Author(s):  
Ya Juan Tian ◽  
Hua Xian Pan ◽  
Xuan Chao Liu ◽  
Guo Jian Cheng
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Selection Process ◽  
Feedforward Neural Networks ◽  
Parameter Selection ◽  
Classification Model ◽  
Support Vector ◽  
New Learning ◽  
Learning Machine ◽  
Hidden Layer

To overcome the problem of lower training speed and difficulty parameter selection in traditional support vector machine (SVM), a method based on extreme learning machine (ELM) for lithofacies recognition is presented in this paper. ELM is a new learning algorithm with single-hidden layer feedforward neural networks (SLFNN). Not only it can simplify the parameter selection process, but also improve the training speed of the network learning. By determining the optimal parameters, the lithofacies classification model is established, and the classification result of ELM is also compared to traditional SVM. The experimental results show that, ELM with less number of neurons has similar classification accuracy compared to SVM, and it is easier to select the parameters which significantly reduce the training speed. The feasibility of ELM for lithofacies recognition and the availability of the algorithm are verified and validated

AN IMPROVEMENT OF EXTREME LEARNING MACHINE FOR COMPACT SINGLE-HIDDEN-LAYER FEEDFORWARD NEURAL NETWORKS

2008 ◽  
Vol 18 (05) ◽  
pp. 433-441 ◽  
Author(s):  
HIEU TRUNG HUYNH ◽  
YONGGWAN WON ◽  
JUNG-JA KIM
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
High Speed ◽  
Learning Algorithm ◽  
Differential Evolution Algorithm ◽  
Feedforward Neural Networks ◽  
Analytical Determination ◽  
Data Sets ◽  
Learning Machine ◽  
Hidden Layer

Recently, a novel learning algorithm called extreme learning machine (ELM) was proposed for efficiently training single-hidden-layer feedforward neural networks (SLFNs). It was much faster than the traditional gradient-descent-based learning algorithms due to the analytical determination of output weights with the random choice of input weights and hidden layer biases. However, this algorithm often requires a large number of hidden units and thus slowly responds to new observations. Evolutionary extreme learning machine (E-ELM) was proposed to overcome this problem; it used the differential evolution algorithm to select the input weights and hidden layer biases. However, this algorithm required much time for searching optimal parameters with iterative processes and was not suitable for data sets with a large number of input features. In this paper, a new approach for training SLFNs is proposed, in which the input weights and biases of hidden units are determined based on a fast regularized least-squares scheme. Experimental results for many real applications with both small and large number of input features show that our proposed approach can achieve good generalization performance with much more compact networks and extremely high speed for both learning and testing.

Study on Deep Structure of Extreme Learning Machine (DS-ELM) for Datasets with Noise

2014 ◽  
Vol 989-994 ◽  
pp. 3679-3682 ◽  
Author(s):  
Meng Meng Ma ◽  
Bo He
Keyword(s):  
Extreme Learning Machine ◽  
Simple Structure ◽  
Learning Algorithm ◽  
Deep Structure ◽  
Noisy Data ◽  
Machine Learning Algorithm ◽  
Feed Forward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer ◽  
Auto Associative Neural Network

Extreme learning machine (ELM), a relatively novel machine learning algorithm for single hidden layer feed-forward neural networks (SLFNs), has been shown competitive performance in simple structure and superior training speed. To improve the effectiveness of ELM for dealing with noisy datasets, a deep structure of ELM, short for DS-ELM, is proposed in this paper. DS-ELM contains three level networks (actually contains three nets ): the first level network is trained by auto-associative neural network (AANN) aim to filter out noise as well as reduce dimension when necessary; the second level network is another AANN net aim to fix the input weights and bias of ELM; and the last level network is ELM. Experiments on four noisy datasets are carried out to examine the new proposed DS-ELM algorithm. And the results show that DS-ELM has higher performance than ELM when dealing with noisy data.

FUSION OF EXTREME LEARNING MACHINE WITH FUZZY INTEGRAL

2013 ◽  
Vol 21 (supp02) ◽  
pp. 23-34 ◽  
Author(s):  
JUNHAI ZHAI ◽  
HONGYU XU ◽  
YAN LI
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Fuzzy Integral ◽  
Fast Learning ◽  
Original Dataset ◽  
Learning Speed ◽  
Feed Forward Neural Networks ◽  
Three Stages ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is an efficient and practical learning algorithm used for training single hidden layer feed-forward neural networks (SLFNs). ELM can provide good generalization performance at extremely fast learning speed. However, ELM suffers from instability and over-fitting, especially on relatively large datasets. Based on probabilistic SLFNs, an approach of fusion of extreme learning machine (F-ELM) with fuzzy integral is proposed in this paper. The proposed algorithm consists of three stages. Firstly, the bootstrap technique is employed to generate several subsets of original dataset. Secondly, probabilistic SLFNs are trained with ELM algorithm on each subset. Finally, the trained probabilistic SLFNs are fused with fuzzy integral. The experimental results show that the proposed approach can alleviate to some extent the problems mentioned above, and can increase the prediction accuracy.

scholarly journals Online Regularized and Kernelized Extreme Learning Machines with Forgetting Mechanism

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xinran Zhou ◽  
Zijian Liu ◽  
Congxu Zhu
Keyword(s):  
Extreme Learning Machine ◽  
Feedforward Neural Networks ◽  
Matrix Inversion ◽  
Time Varying ◽  
Extreme Learning Machines ◽  
Learning Machines ◽  
Matrix Expansion ◽  
Learning Machine ◽  
Hidden Layer ◽  
Forgetting Mechanism

To apply the single hidden-layer feedforward neural networks (SLFN) to identify time-varying system, online regularized extreme learning machine (ELM) with forgetting mechanism (FORELM) and online kernelized ELM with forgetting mechanism (FOKELM) are presented in this paper. The FORELM updates the output weights of SLFN recursively by using Sherman-Morrison formula, and it combines advantages of online sequential ELM with forgetting mechanism (FOS-ELM) and regularized online sequential ELM (ReOS-ELM); that is, it can capture the latest properties of identified system by studying a certain number of the newest samples and also can avoid issue of ill-conditioned matrix inversion by regularization. The FOKELM tackles the problem of matrix expansion of kernel based incremental ELM (KB-IELM) by deleting the oldest sample according to the block matrix inverse formula when samples occur continually. The experimental results show that the proposed FORELM and FOKELM have better stability than FOS-ELM and have higher accuracy than ReOS-ELM in nonstationary environments; moreover, FORELM and FOKELM have time efficiencies superiority over dynamic regression extreme learning machine (DR-ELM) under certain conditions.

scholarly journals Elliot and Symmetric Elliot Extreme Learning Machines for Gaussian Noisy Industrial Thermal Modelling

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 90 ◽  
Author(s):  
Jose Salmeron ◽  
Antonio Ruiz-Celma
Keyword(s):  
Extreme Learning Machine ◽  
Support Vector ◽  
Thermal Processes ◽  
Drying Process ◽  
Machine Model ◽  
Learning Machines ◽  
Nearest Neighbours ◽  
Thermal Drying ◽  
Learning Machine ◽  
Hidden Layer

This research proposes an Elliot-based Extreme Learning Machine approach for industrial thermal processes regression. The main contribution of this paper is to propose an Extreme Learning Machine model with Elliot and Symmetric Elliot activation functions that will look for the fittest number of neurons in the hidden layer. The methodological proposal is tested on an industrial thermal drying process. The thermal drying process is relevant in many industrial processes such as the food industry, biofuels production, detergents and dyes in powder production, pharmaceutical industry, reprography applications, textile industries and others. The methodological proposal of this paper outperforms the following techniques: Linear Regression, k-Nearest Neighbours regression, Regression Trees, Random Forest and Support Vector Regression. In addition, all the experiments have been benchmarked using four error measurements (MAE, MSE, MEADE, R 2 ).

scholarly journals A Comparison Study of Extreme Learning Machine and Least Squares Support Vector Machine for Structural Impact Localization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Qingsong Xu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Support Vector ◽  
Comparison Study ◽  
Fast Learning ◽  
Structural Impact ◽  
Learning Machine ◽  
Impact Localization

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural network dedicated to an extremely fast learning. However, the performance of ELM in structural impact localization is unknown yet. In this paper, a comparison study of ELM with least squares support vector machine (LSSVM) is presented for the application on impact localization of a plate structure with surface-mounted piezoelectric sensors. Both basic and kernel-based ELM regression models have been developed for the location prediction. Comparative studies of the basic ELM, kernel-based ELM, and LSSVM models are carried out. Results show that the kernel-based ELM requires the shortest learning time and it is capable of producing suboptimal localization accuracy among the three models. Hence, ELM paves a promising way in structural impact detection.

scholarly journals A Hybrid Model of Extreme Learning Machine Based on Bat and Cuckoo Search Algorithm for Regression and Multiclass Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qinwei Fan ◽  
Tongke Fan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Classification Problems ◽  
Neural Network Learning ◽  
Practical Applications ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM), as a new simple feedforward neural network learning algorithm, has been extensively used in practical applications because of its good generalization performance and fast learning speed. However, the standard ELM requires more hidden nodes in the application due to the random assignment of hidden layer parameters, which in turn has disadvantages such as poorly hidden layer sparsity, low adjustment ability, and complex network structure. In this paper, we propose a hybrid ELM algorithm based on the bat and cuckoo search algorithm to optimize the input weight and threshold of the ELM algorithm. We test the numerical experimental performance of function approximation and classification problems under a few benchmark datasets; simulation results show that the proposed algorithm can obtain significantly better prediction accuracy compared to similar algorithms.

scholarly journals Peramalan Indeks Harga Saham Gabungan (IHSG) dengan Metode Extreme Learning Machine (ELM)

2017 ◽  
Vol 1 (1) ◽  
pp. 22-32
Author(s):  
Afifah Arifianty ◽  
Mulyono Mulyono ◽  
Med Irzal
Keyword(s):  
Neural Networks ◽  
Extreme Learning Machine ◽  
Feedforward Neural Networks ◽  
Learning Machine ◽  
Hidden Layer

Abstrak Indeks Harga Saham Gabungan (IHSG) merupakan suatu nilai untuk mengukur kinerja seluruh saham. IHSG mencerminkan perkembangan pasar secara keseluruhan. Jika IHSG mengalami kenaikan dari hari kemarin maka dapat disimpulkan beberapa saham yang berada pada bursa efek mengalami kenaikan. Oleh karena itu, peramalan harga akan sangat bermanfaat untuk para investor, sehingga mereka dapat mengetahui prospek investasi saham di masa datang. Ada banyak metode untuk peramalan. Tetapi, metode-metode yang telah ada sebelumnya membutuhkan waktu komputasi yang relatif lebih lama. Metode Jaringan Syaraf Tiruan(JST) dikhawatirkan akan semakin ditinggalkan karena diperlukan waktu yang lama dalam pengambilan keputusan. Untuk mengatasi masalah, Huang (2004) menemukan sebuah metode pembelajaran dalam JST bernama Extreme Learning Machine (ELM). ELM merupakan jaringan syaraf tiruan feedforward dengan satu hidden layer atau lebih dikenal dengan istilah Single hidden Layer Feedforward neural Networks(SLFNs) (Sun et al, 2008). Pada metode ini, faktor yang digunakan dalam peramalan hanya faktor data masa lalu, bukan disebabkan faktor lain seperti politik, ekonomi dan lain-lain. Kata kunci: Indeks Harga Saham Gabungan, Peramalan, Jaringan Syaraf Tiruan, Extreme Learning Machine.

Comparison of the Extreme Learning Machine with the BP Neural Network for Short-Term Prediction of Wind Power

2012 ◽  
Vol 608-609 ◽  
pp. 564-568 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Meng Lin Zhang ◽  
Xiao Lu Gong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Power Prediction ◽  
Feed Forward Neural Network ◽  
Wind Power Prediction ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new and effective single-hidden layer feed forward neural network learning algorithm. Extreme learning machine only needs to set the number of hidden layer nodes of the network, and there is no need to adjust the neural network input weights and the hidden units bias, and it generates the only optimum solution, so it has the advantage of fast learning and good generalization ability. And the back propagation (BP) neural network is the most maturely applied. This paper has introduced the extreme learning machine into the wind power prediction. By comparing the wind power prediction method using the BP neural network. Study shows that the extreme learning machine has better prediction accuracy and shorter model training time.

scholarly journals A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Dong Xiao ◽  
Beijing Li ◽  
Yachun Mao
Keyword(s):  
Extreme Learning Machine ◽  
Least Squares Method ◽  
Learning Algorithm ◽  
Average Precision ◽  
Machine Method ◽  
Rapid Learning ◽  
Actual Output ◽  
Input Layer ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

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