scholarly journals A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Imen Jammoussi ◽  
Mounir Ben Nasr
Keyword(s):  
Extreme Learning Machine ◽  
Inverse Method ◽  
Learning Algorithm ◽  
Self Organizing Map ◽  
Random Parameters ◽  
Fast Learning ◽  
Self Organized ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons

Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In order to select a suitable number of hidden neurons, this paper proposes a novel hybrid learning based on a two-step process. First, the parameters of hidden layer are adjusted by a self-organized learning algorithm. Next, the weights matrix of the output layer is determined using the Moore–Penrose inverse method. Nine classification datasets are considered to demonstrate the efficiency of the proposed approach compared with original extreme learning machine, Tikhonov regularization optimally pruned extreme learning machine, and backpropagation algorithms. The results show that the proposed method is fast and produces better accuracy and generalization performances.

Two-Motor Synchronous Decoupling Control Based on Improved Incremental Regularized Extreme Learning Machine

2013 ◽  
Vol 765-767 ◽  
pp. 1854-1857
Author(s):  
Feng Wang ◽  
Jin Lin Ding ◽  
Hong Sun
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Decoupling Control ◽  
Loop Control ◽  
Fast Learning ◽  
Optimal Network ◽  
Learning Machine ◽  
Hidden Layer ◽  
Hidden Neurons

Neural network generalized inverse (NNGI) can realize two-motor synchronous decoupling control, but traditional neural network (NN) exists many shortcomings, Regular extreme learning machine (RELM) has fast learning and good generalization ability, which is an ideal approach to approximate inverse system. But it is difficult to accurately give the reasonable number of hidden neurons. Improved incremental RELM(IIRELM) is prospected on the basis of analyzing RELM learning algorithm, which can automatically determine optimal network structure through gradually adding new hidden-layer neurons, and prediction model based on IIRELM is applied in two-motor closed-loop control based on NNGI, the decoupling control between velocity and tension is realized. The experimental results proved that the system has excellent performance.

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 An Improved Extreme Learning Machine Based on Full Rank Cholesky Factorization

2018 ◽  
Vol 246 ◽  
pp. 03018
Author(s):  
Zuozhi Liu ◽  
JinJian Wu ◽  
Jianpeng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Computational Cost ◽  
Full Rank ◽  
Cholesky Factorization ◽  
Fast Learning ◽  
New Novel ◽  
Learning Speed ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a new novel learning algorithm for generalized single-hidden layer feedforward networks (SLFNs). Although it shows fast learning speed in many areas, there is still room for improvement in computational cost. To address this issue, this paper proposes an improved ELM (FRCFELM) which employs the full rank Cholesky factorization to compute output weights instead of traditional SVD. In addition, this paper proves in theory that the proposed FRCF-ELM has lower computational complexity. Experimental results over some benchmark applications indicate that the proposed FRCF-ELM learns faster than original ELM algorithm while preserving good generalization performance.

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.

HONNs with Extreme Learning Machine to Handle Incomplete Datasets

2013 ◽  
pp. 276-292
Author(s):  
Shuxiang Xu
Keyword(s):  
Neural Networks ◽  
Missing Data ◽  
Extreme Learning Machine ◽  
Real Life ◽  
Bankruptcy Prediction ◽  
Share Prices ◽  
Fast Learning ◽  
Business Cases ◽  
Learning Machine ◽  
Hidden Layer

An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a very fast learning speed: it can learn thousands of times faster than conventionally popular learning algorithms (Huang, et al., 2006). Artificial Neural Networks (ANNs) have been widely used as powerful information processing models and adopted in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents, and many more. Higher Order Neural Networks (HONNs) are ANNs in which the net input to a computational neuron is a weighted sum of products of its inputs. Real life data are not usually perfect. They contain wrong, incomplete, or vague data. Hence, it is usual to find missing data in many information sources used. Missing data is a common problem in statistical analysis (Little & Rubin, 1987). This chapter uses the Extreme Learning Machine (ELM) algorithm for HONN models and applies it in several significant business cases, which involve missing datasets. The experimental results demonstrate that HONN models with the ELM algorithm offer significant advantages over standard HONN models, such as faster training, as well as improved generalization abilities.

scholarly journals A Robust AdaBoost.RT Based Ensemble Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Pengbo Zhang ◽  
Zhixin Yang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Approximation Accuracy ◽  
Weak Learner ◽  
Fast Learning ◽  
Learning Speed ◽  
Regression Problems ◽  
Learning Machine ◽  
The Stability ◽  
Stability And Accuracy

Extreme learning machine (ELM) has been well recognized as an effective learning algorithm with extremely fast learning speed and high generalization performance. However, to deal with the regression applications involving big data, the stability and accuracy of ELM shall be further enhanced. In this paper, a new hybrid machine learning method called robust AdaBoost.RT based ensemble ELM (RAE-ELM) for regression problems is proposed, which combined ELM with the novel robust AdaBoost.RT algorithm to achieve better approximation accuracy than using only single ELM network. The robust threshold for each weak learner will be adaptive according to the weak learner’s performance on the corresponding problem dataset. Therefore, RAE-ELM could output the final hypotheses in optimally weighted ensemble of weak learners. On the other hand, ELM is a quick learner with high regression performance, which makes it a good candidate of “weak” learners. We prove that the empirical error of the RAE-ELM is within a significantly superior bound. The experimental verification has shown that the proposed RAE-ELM outperforms other state-of-the-art algorithms on many real-world regression problems.

Structure Impact Localization Using Emerging Artificial Intelligence Algorithms

2015 ◽  
pp. 103-123
Author(s):  
Qingsong Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Learning Algorithm ◽  
Experimental Studies ◽  
Back Propagation ◽  
Damage Localization ◽  
Localization Accuracy ◽  
Fast Learning ◽  
Learning Machine ◽  
Hidden Layer

Extreme learning machine (ELM) is a learning algorithm for single-hidden layer feedforward neural networks. In theory, this algorithm is able to provide good generalization capability at extremely fast learning speed. Comparative studies of benchmark function approximation problems revealed that ELM can learn thousands of times faster than conventional neural network (NN) and can produce good generalization performance in most cases. Unfortunately, the research on damage localization using ELM is limited in the literature. In this chapter, the ELM is extended to the domain of damage localization of plate structures. Its effectiveness in comparison with typical neural networks such as back-propagation neural network (BPNN) and least squares support vector machine (LSSVM) is illustrated through experimental studies. Comparative investigations in terms of learning time and localization accuracy are carried out in detail. It is shown that ELM paves a new way in the domain of plate structure health monitoring. Both advantages and disadvantages of using ELM are discussed.

A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

2019 ◽  
Vol 36 (4) ◽  
pp. 3263-3269 ◽  
Author(s):  
Chunmei He ◽  
Yaqi Liu ◽  
Tong Yao ◽  
Fanhua Xu ◽  
Yanyun Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Fast Learning ◽  
Fuzzy Neural ◽  
Learning Machine

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 Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Chao Wang ◽  
Jianhui Wang ◽  
Shusheng Gu
Keyword(s):  
Least Squares ◽  
Extreme Learning Machine ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Complex Data ◽  
Classification Problems ◽  
Deep Network ◽  
Least Squares Solution ◽  
Fast Learning ◽  
Learning Machine

Extreme learning machine (ELM) as an emerging technology has recently attracted many researchers’ interest due to its fast learning speed and state-of-the-art generalization ability in the implementation. Meanwhile, the incremental extreme learning machine (I-ELM) based on incremental learning algorithm was proposed which outperforms many popular learning algorithms. However, the incremental algorithms with ELM do not recalculate the output weights of all the existing nodes when a new node is added and cannot obtain the least-squares solution of output weight vectors. In this paper, we propose orthogonal convex incremental learning machine (OCI-ELM) with Gram-Schmidt orthogonalization method and Barron’s convex optimization learning method to solve the nonconvex optimization problem and least-squares solution problem, and then we give the rigorous proofs in theory. Moreover, in this paper, we propose a deep architecture based on stacked OCI-ELM autoencoders according to stacked generalization philosophy for solving large and complex data problems. The experimental results verified with both UCI datasets and large datasets demonstrate that the deep network based on stacked OCI-ELM autoencoders (DOC-IELM-AEs) outperforms the other methods mentioned in the paper with better performance on regression and classification problems.

Sign in / Sign up

Export Citation Format

Share Document