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.

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.

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.

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

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 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.

scholarly journals One-Class Classification with Extreme Learning Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Qian Leng ◽  
Honggang Qi ◽  
Jun Miao ◽  
Wentao Zhu ◽  
Guiping Su
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Classification Problem ◽  
Traditional Learning ◽  
Learning Speed ◽  
One Class Classifier ◽  
Learning Machine ◽  
Hidden Layer ◽  
One Class Classification ◽  
Network Approaches

One-class classification problem has been investigated thoroughly for past decades. Among one of the most effective neural network approaches for one-class classification, autoencoder has been successfully applied for many applications. However, this classifier relies on traditional learning algorithms such as backpropagation to train the network, which is quite time-consuming. To tackle the slow learning speed in autoencoder neural network, we propose a simple and efficient one-class classifier based on extreme learning machine (ELM). The essence of ELM is that the hidden layer need not be tuned and the output weights can be analytically determined, which leads to much faster learning speed. The experimental evaluation conducted on several real-world benchmarks shows that the ELM based one-class classifier can learn hundreds of times faster than autoencoder and it is competitive over a variety of one-class classification methods.

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