scholarly journals Forecasting Gold Prices Based on Extreme Learning Machine

2016 ◽  
Vol 11 (3) ◽  
pp. 372 ◽  
Author(s):  
Kumar Chandar Sivalingam ◽  
Sumathi Mahendran ◽  
Sivanandam Natarajan
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Ability ◽  
Stock Index ◽  
Feed Forward Back Propagation ◽  
Crude Oil Prices ◽  
The Future ◽  
Good Learning ◽  
Learning Machine

<p>In recent years, the investors pay major attention to invest in gold market ecause of huge profits in the future. Gold is the only commodity which maintains ts value even in the economic and financial crisis. Also, the gold prices are closely elated with other commodities. The future gold price prediction becomes the warning ystem for the investors due to unforeseen risk in the market. Hence, an accurate gold rice forecasting is required to foresee the business trends. This paper concentrates on orecasting the future gold prices from four commodities like historical data’s of gold rices, silver prices, Crude oil prices, Standard and Poor’s 500 stock index (S&amp;P500) ndex and foreign exchange rate. The period used for the study is from 1st January 000 to 31st April 2014. In this paper, a learning algorithm for single hidden layered eed forward neural networks called Extreme Learning Machine (ELM) is used which as good learning ability. Also, this study compares the five models namely Feed orward networks without feedback, Feed forward back propagation networks, Radial asis function, ELMAN networks and ELM learning model. The results prove that he ELM learning performs better than the other methods.</p>

scholarly journals Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Zhiming Gui ◽  
Haipeng Yu
Keyword(s):  
Travel Time ◽  
Extreme Learning Machine ◽  
Road Networks ◽  
Time Estimation ◽  
Learning Ability ◽  
Prediction Algorithm ◽  
Travel Time Estimation ◽  
Trace Data ◽  
Learning Machine ◽  
Travel Time Forecasting

Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

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.

Prediction of Software Defect Using Ensemble Learning Based Improved Sparrow Search Algorithm To Optimize Extreme Learning Machine

2021 ◽  
Author(s):  
Yu Tang ◽  
Qi Dai ◽  
Mengyuan Yang ◽  
Lifang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Prediction Ability ◽  
Software Defect ◽  
Ensemble Learning Algorithm ◽  
Learning Machine

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

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.

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 Convergence Analysis of An Improved Extreme Learning Machine Based on Gradient Descent Method

2016 ◽  
Vol 8 (1) ◽  
pp. 5-15
Author(s):  
Liu Yusong ◽  
Su Zhixun ◽  
Zhang Bingjie ◽  
Gong Xiaoling ◽  
Sang Zhaoyang
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Error Function ◽  
Gradient Descent Method ◽  
Testing Time ◽  
Training Procedure ◽  
Feed Forward Neural Network ◽  
Learning Machine ◽  
Theoretical Results ◽  
Hidden Nodes

Abstract Extreme learning machine (ELM) is an efficient algorithm, but it requires more hidden nodes than the BP algorithms to reach the matched performance. Recently, an efficient learning algorithm, the upper-layer-solution-unaware algorithm (USUA), is proposed for the single-hidden layer feed-forward neural network. It needs less number of hidden nodes and testing time than ELM. In this paper, we mainly give the theoretical analysis for USUA. Theoretical results show that the error function monotonously decreases in the training procedure, the gradient of the error function with respect to weights tends to zero (the weak convergence), and the weight sequence goes to a fixed point (the strong convergence) when the iterations approach positive infinity. An illustrated simulation has been implemented on the MNIST database of handwritten digits which effectively verifies the theoretical results..

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