Dual Extreme Learning Machines-Based Spatiotemporal Modeling for Nonlinear Distributed Thermal Processes

2020 ◽  
Vol 18 (01) ◽  
pp. 2050026
Author(s):  
Xi Jin ◽  
Hai Dong Yang ◽  
Kang Kang Xu ◽  
Cheng Jiu Zhu
Keyword(s):  
Original System ◽  
Least Square ◽  
Distribution Model ◽  
Support Vector ◽  
Distributed Parameter ◽  
Thermal Processes ◽  
Spatiotemporal Modeling ◽  
Online Application ◽  
Learning Machines ◽  
Learning Machine

Many industrial thermal processes belong to distributed parameter systems (DPSs), which have two coupled nonlinear blocks. Dual least square support vector machines (LS-SVM) has been proposed to model such systems. However, due to the use of two LS-SVM, this method often leads to heavy computation and long learning time, which does not suit for online application. In this study, a dual extreme learning machine (ELM)-based spatiotemporal modeling method is proposed for such two nonlinearities embedded DPSs. Firstly, the KL method is applied to reduce the dimension of the original system and obtain the spatial basis functions (BFs). Then, dual ELM is designed to match the two nonlinear structures. Finally, through the reconstruction of space–time synthesis, the approximate spatiotemporal distribution model of the original system is obtained. In addition, simulations on a curing process is studied to confirm the effectiveness of the proposed method.

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 ).

Comparison of Several Extreme Learning Machine Algorithm for Modeling Concrete Compressive Strength

2014 ◽  
Vol 548-549 ◽  
pp. 1735-1738 ◽  
Author(s):  
Jian Tang ◽  
Dong Yan ◽  
Li Jie Zhao
Keyword(s):  
Compressive Strength ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Partial Least Square ◽  
Least Square ◽  
Extreme Learning Machines ◽  
Concrete Compressive Strength ◽  
Learning Machines ◽  
Learning Machine

Modeling concrete compressive strength is useful to ensure quality of civil engineering. This paper aims to compare several Extreme learning machines (ELMs) based modeling approaches for predicting the concrete compressive strength. Normal ELM algorithm, Partial least square-based extreme learning machines (PLS-ELMs) algorithm and Kernel ELM (KELM) algorithm are used and evaluated. Results indicate that the normal ELMs algorithm has the highest modeling speed, and the KELM has the best prediction accuracy. Every method is validated for modeling concrete compressive strength. The appropriate modeling approach should be selected according different purposes.

scholarly journals Cascade-based multi-scale AI approach for modeling rainfall-runoff process

2017 ◽  
Vol 49 (4) ◽  
pp. 1191-1207 ◽  
Author(s):  
Vahid Nourani ◽  
Gholamreza Andalib ◽  
Fahreddin Sadikoglu ◽  
Elnaz Sharghi
Keyword(s):  
Least Square ◽  
Support Vector ◽  
Self Organizing Map ◽  
Rainfall Runoff ◽  
Feed Forward Neural Network ◽  
Multi Scale ◽  
Runoff Prediction ◽  
Som Clustering ◽  
R Process ◽  
Learning Machine

Abstract In this paper, runoff time series of the sub-basins in a cascade form were decomposed by Wavelet Transform (WT) to extract their dynamical and multi-scale features for modeling Multi-Station (MS) rainfall-runoff (R-R) process of the Little River Watershed (LRW) in USA. A Self-Organizing Map (SOM) clustering technique was also employed to find homogeneous extracted sub-series' clusters. As a complementary feature, extraction criterion of mutual information (MI) was utilized for proper cluster agent choice to impose to the artificial intelligence (AI) models (Feed Forward Neural Network, FFNN; Extreme Learning Machine, ELM; and Least Square Support Vector Machine, LSSVM) to predict the runoff of the LRW sub-basins. The performance of wavelet-based runoff prediction was compared to the Markovian-based MS model. The proposed method not only considers the prediction of the outlet runoff but also covers predictions of interior sub-basins behavior. The outcomes showed that the proposed AI-models combined with the SOM and MI tools enhanced the MS runoff prediction efficiency up to 23% in comparison with the Markovian-based models. Nevertheless, benefit of the seasonality of the process along with reduction of dimension of the inputs could help the AI-models to consume pure information of the recorded data.

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

Spectroscopy quantitative analysis cotton content of blend fabrics

2016 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Xudong Sun ◽  
Mingxing Zhou ◽  
Yize Sun
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Content Type ◽  
Svm Model ◽  
Calibration Methods ◽  
Learning Machine ◽  
Mean Square Errors

Purpose – The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics. Design/methodology/approach – In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r p ) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively. Findings – The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics. Originality/value – It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

Decoupling Control of Three Degrees of Freedom Hybrid Magnetic Bearing Based on LS-SVM

2014 ◽  
Vol 529 ◽  
pp. 534-538
Author(s):  
Yuan Yuan Li ◽  
Huang Qiu Zhu
Keyword(s):  
Degrees Of Freedom ◽  
Control Method ◽  
Hybrid Control ◽  
Original System ◽  
Magnetic Bearing ◽  
Inverse System ◽  
Least Square ◽  
Decoupling Control ◽  
Support Vector ◽  
Three Degrees Of Freedom

In the paper, the decoupling control method based on least square support vector machine (LS-SVM) inverse system is proposed, and adopting the method realizes decoupling control of an AC-DC three degrees of freedom hybrid magnetic bearing (AC-DC-3DOF-HMB). Aimed at the complicated multivariate nonlinear, strong coupling system of the AC-DC-3DOF-HMB, the reversibility of original system was analyzed, by the ability of least square support vector machines (LS-SVM) in universal approximation and identification fitting to get inverse model of AC-DC three degrees of freedom hybrid magnetic bearing. Then according to the basic principle of inverse system method, the inverse system was connected with the original system. So the complex nonlinear multivariable system is decoupled into three independent pseudo-linear system. The simulation results show that the system was decoupled; the hybrid control method has good dynamic and static performance, verify the feasibility of the proposed control method.

ELM Regularized Method for Classification Problems

2016 ◽  
Vol 25 (01) ◽  
pp. 1550026 ◽  
Author(s):  
Juan J. Carrasco ◽  
Mónica Millán-Giraldo ◽  
Juan Caravaca ◽  
Pablo Escandell-Montero ◽  
José M. Martínez-Martínez ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Single Layer ◽  
Least Square ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Problem Solution ◽  
Classification Problems ◽  
Training Time ◽  
Learning Techniques ◽  
Learning Machine

Extreme Learning Machine (ELM) is a recently proposed algorithm, efficient and fast for learning the parameters of single layer neural structures. One of the main problems of this algorithm is to choose the optimal architecture for a given problem solution. To solve this limitation several solutions have been proposed in the literature, including the regularization of the structure. However, to the best of our knowledge, there are no works where such adjustment is applied to classification problems in the presence of a non-linearity in the output; all published works tackle modelling or regression problems. Our proposal has been applied to a series of standard databases for the evaluation of machine learning techniques. Results obtained in terms of classification success rate and training time, are compared to the original ELM, to the well known Least Square Support Vector Machine (LS-SVM) algorithm and with two other methods based on the ELM regularization: Optimally Pruned Extreme Learning Machine (OP-ELM) and Bayesian Extreme Learning Machine (BELM). The obtained results clearly demonstrate the usefulness of the proposed method and its superiority over a classical approach.

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