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 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 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

scholarly journals Toward deep neural networks: Mirror extreme learning machines for pattern classification

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 4985-4996
Author(s):  
Bolin Liao ◽  
Chuan Ma ◽  
Meiling Liao ◽  
Shuai Li ◽  
Zhiguan Huang
Keyword(s):  
Neural Network ◽  
Extreme Learning Machine ◽  
Pattern Classification ◽  
Simple Structure ◽  
Layer Structure ◽  
Feed Forward Neural Network ◽  
Learning Machines ◽  
S Parameter ◽  
Learning Machine ◽  
Hidden Layer

In this paper, a novel type of feed-forward neural network with a simple structure is proposed and investigated for pattern classification. Because the novel type of forward neural network?s parameter setting is mirrored with those of the Extreme Learning Machine (ELM), it is termed the mirror extreme learning machine (MELM). For the MELM, the input weights are determined by the pseudoinverse method analytically, while the output weights are generated randomly, which are completely different from the conventional ELM. Besides, a growing method is adopted to obtain the optimal hidden-layer structure. Finally, to evaluate the performance of the proposed MELM, abundant comparative experiments based on different real-world classification datasets are performed. Experimental results validate the high classification accuracy and good generalization performance of the proposed neural network with a simple structure in pattern classification.

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.

Predicting Surface Roughness in Turning Operation Using Extreme Learning Machine

2014 ◽  
Vol 554 ◽  
pp. 431-435 ◽  
Author(s):  
Ahmad Nooraziah ◽  
V. Janahiraman Tiagrajah
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Extreme Learning Machine ◽  
Cutting Parameters ◽  
Feedforward Neural Network ◽  
Support Vector ◽  
Satisfactory Solution ◽  
Modeling Techniques ◽  
Learning Machine ◽  
Hidden Layer

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection.

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

A novel hybrid model using the rotation forest-based differential evolution online sequential extreme learning machine for illumination correction of dyed fabrics

2018 ◽  
Vol 89 (7) ◽  
pp. 1180-1197 ◽  
Author(s):  
Zhiyu Zhou ◽  
Xu Gao ◽  
Jianxin Zhang ◽  
Zefei Zhu ◽  
Xudong Hu
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Differential Evolution Algorithm ◽  
Image Features ◽  
Support Vector ◽  
Rotation Forest ◽  
Generalization Ability ◽  
Learning Machine ◽  
Hidden Layer

This study proposes an ensemble differential evolution online sequential extreme learning machine (DE-OSELM) for textile image illumination correction based on the rotation forest framework. The DE-OSELM solves the inaccuracy and long training time problems associated with traditional illumination correction algorithms. First, the Grey–Edge framework is used to extract the low-dimensional and efficient image features as online sequential extreme learning machine (OSELM) input vectors to improve the training and learning speed of the OSELM. Since the input weight and hidden-layer bias of OSELMs are randomly obtained, the OSELM algorithm has poor prediction accuracy and low robustness. To overcome this shortcoming, a differential evolution algorithm that has the advantages of good global search ability and robustness is used to optimize the input weight and hidden-layer bias of the DE-OSELM. To further improve the generalization ability and robustness of the illumination correction model, the rotation forest algorithm is used as the ensemble framework, and the DE-OSELM is used as the base learner to replace the regression tree algorithm in the original rotation forest algorithm. Then, the obtained multiple different DE-OSELM learners are aggregated to establish the prediction model. The experimental results show that compared with the textile color correction algorithm based on the support vector regression and extreme learning machine algorithms, the ensemble illumination correction method achieves high prediction accuracy, strong robustness, and good generalization ability.

Artificial bee colony algorithm–optimized error minimized extreme learning machine and its application in short-term wind speed prediction

2018 ◽  
Vol 43 (3) ◽  
pp. 263-276 ◽  
Author(s):  
Zhongda Tian ◽  
Gang Wang ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Wind Speed ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Short Term ◽  
Machine Model ◽  
Bee Colony ◽  
Learning Machine ◽  
Hidden Layer

In order to improve the prediction accuracy of short-term wind speed, a short-term wind speed prediction model based on artificial bee colony algorithm optimized error minimized extreme learning machine model is proposed. The extreme learning machine has the advantages of fast learning speed and strong generalization ability. But many useless neurons of incremental extreme learning machine have little influences on the final output and, at the same time, reduce the efficiency of the algorithm. The optimal parameters of the hidden layer nodes will make network output error of incremental extreme learning machine decrease with fast speed. Based on the error minimized extreme learning machine, artificial bee colony algorithm is introduced to optimize the parameters of the hidden layer nodes, decrease the number of useless neurons, reduce training and prediction error, achieve the goal of reducing the network complexity, and improve the efficiency of the algorithm. The error minimized extreme learning machine prediction model is constructed with the obtained optimal parameters. The stability and convergence property of artificial bee colony algorithm optimized error minimized extreme learning machine model are proved. The practical short-term wind speed time series is used as the research object and to verify the validity of the prediction model. Multi-step prediction simulation of short-term wind speed is carried out. Compared with other prediction models, simulation results show that the prediction model proposed in this article reduces the training time of the prediction model and decreases the number of hidden layer nodes. The prediction model has higher prediction accuracy and reliability performance, meanwhile improves the performance indicators.

scholarly journals Improved Two Hidden Layers Extreme Learning Machines for Node Localization in Range Free Wireless Sensor Networks

2021 ◽  
pp. 528-534
Author(s):  
Oumaima Liouane ◽  
◽  
Smain Femmam ◽  
Toufik Bakir ◽  
Abdessalem Ben Abdelali
Keyword(s):  
Extreme Learning Machine ◽  
Wireless Sensor ◽  
Isotropic Case ◽  
Localization Algorithm ◽  
Node Localization ◽  
Practical Applications ◽  
Learning Machines ◽  
Learning Machine ◽  
Hidden Layer ◽  
Range Free

Wireless Sensor Network (WSN) architectures are widely used in a variety of practical applications. In most cases of application, the event information transmitted by a sensor node via the network has no significance without the knowledge of its accurate geographical localization. In this paper, a method based on Machine Learning Technique (MLT) is proposed to improve node accuracy localization in WSN. We propose a Single Hidden Layer Extreme Learning Machine (SHL-ELM) and a Two Hidden Layer Extreme Learning Machine (THL-ELM) based methods for nodes localization in WSN. The suggested methods are applied in different Multi-hop WSN deployment cases. We focused on range-free localization algorithm in isotropic case and irregular environments. Simulation results demonstrate that the proposed THL-ELM algorithm greatly minimizes the average localization errors when compared to the Single Hidden Layer Extreme Learning Machine and the Distance Vector Hop (DV- Hop) algorithm.

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