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.

scholarly journals Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-26 ◽  
Author(s):  
Wei Xie ◽  
Jie-sheng Wang ◽  
Cheng Xing ◽  
Sha-Sha Guo ◽  
Meng-wei Guo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Extreme Learning Machine ◽  
Prediction Accuracy ◽  
Soft Sensor ◽  
Support Vector ◽  
Structure Parameters ◽  
Generalization Ability ◽  
Sensor Model ◽  
Learning Machine

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

scholarly journals Multistep-Ahead Stock Price Forecasting Based on Secondary Decomposition Technique and Extreme Learning Machine Optimized by the Differential Evolution Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhenpeng Tang ◽  
Tingting Zhang ◽  
Junchuan Wu ◽  
Xiaoxu Du ◽  
Kaijie Chen
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hybrid Model ◽  
Stock Price ◽  
Differential Evolution Algorithm ◽  
Prediction Performance ◽  
Stock Index ◽  
Decomposition Technique ◽  
Mode Decomposition ◽  
Learning Machine

The prediction research of the stock market prices is of great significance. Based on the secondary decomposition techniques of variational mode decomposition (VMD) and ensemble empirical mode decomposition (EEMD), this paper constructs a new hybrid prediction model by combining with extreme learning machine (ELM) optimized by the differential evolution (DE) algorithm. The hybrid model applies VMD technology to the original stock index price sequence to obtain different modal components and the residual item, then applies EEMD technology to the residual item, and then superimposes the prediction results of the DE-ELM model for each modal component and the residual item to obtain the final prediction results. In order to verify the validity of the model, this paper constructs a series of benchmark models and, respectively, tests the samples of the S&P 500 index and the HS300 index by one-step, three-step, and five-step forward forecasting. The empirical results show that the hybrid model proposed in this paper achieves the best prediction performance in all prediction scenarios, which indicates that the modeling idea focusing on the residual term effectively improves the prediction performance of the model. In addition, the prediction effect of the model combined with the decomposition technology is superior to the single DE-ELM model, where the secondary decomposition technique has a significant decomposition advantage compared to the single decomposition technique.

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 Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

2018 ◽  
Vol 25 (4) ◽  
pp. 43 ◽  
Author(s):  
Camila Martins Saporetti ◽  
Grasiele Regina Duarte ◽  
Tales Lima Fonseca ◽  
Leonardo Goliatt Da Fonseca ◽  
Egberto Pereira
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Reservoir Characterization ◽  
Differential Evolution Algorithm ◽  
Activation Function ◽  
Development Planning ◽  
Lithology Identification ◽  
Learning Machine ◽  
The Cost

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning.

scholarly journals Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive-Definite Cholesky Factorization

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingyi Liu ◽  
Ba Tuan Le
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Network Structure ◽  
Prediction Accuracy ◽  
Computational Cost ◽  
Positive Definite ◽  
Cholesky Factorization ◽  
Optimal Network ◽  
Learning Machine ◽  
Hidden Layer

The theory and implementation of extreme learning machine (ELM) prove that it is a simple, efficient, and accurate machine learning method. Compared with other single hidden layer feedforward neural network algorithms, ELM is characterized by simpler parameter selection rules, faster convergence speed, and less human intervention. The multiple hidden layer regularized extreme learning machine (MRELM) inherits these advantages of ELM and has higher prediction accuracy. In the MRELM model, the number of hidden layers is randomly initiated and fixed, and there is no iterative tuning process. However, the optimal number of hidden layers is the key factor to determine the generalization ability of MRELM. Given this situation, it is obviously unreasonable to determine this number by trial and random initialization. In this paper, an incremental MRELM training algorithm (FC-IMRELM) based on forced positive-definite Cholesky factorization is put forward to solve the network structure design problem of MRELM. First, an MRELM-based prediction model with one hidden layer is constructed, and then a new hidden layer is added to the prediction model in each training step until the generalization performance of the prediction model reaches its peak value. Thus, the optimal network structure of the prediction model is determined. In the training procedure, forced positive-definite Cholesky factorization is used to calculate the output weights of MRELM, which avoids the calculation of the inverse matrix and Moore-Penrose generalized inverse of matrix involved in the training process of hidden layer parameters. Therefore, FC-IMRELM prediction model can effectively reduce the computational cost brought by the process of increasing the number of hidden layers. Experiments on classification and regression problems indicate that the algorithm can be effectively used to determine the optimal network structure of MRELM, and the prediction model training by the algorithm has excellent performance in prediction accuracy and computational cost.

scholarly journals Prediction of scour depth around bridge piers using self-adaptive extreme learning machine

2016 ◽  
Vol 19 (2) ◽  
pp. 207-224 ◽  
Author(s):  
Isa Ebtehaj ◽  
Ahmed M. A. Sattar ◽  
Hossein Bonakdari ◽  
Amir Hossein Zaji
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Differential Evolution Algorithm ◽  
Bridge Piers ◽  
Support Vector ◽  
Scour Depth ◽  
Pier Scour ◽  
New Model ◽  
Learning Machine ◽  
Self Adaptive

Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for root mean square error (RMSE) (0.15, 0.65, respectively) and mean absolute relative error (MARE) (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.

Research on Optimization Method of Extreme Learning Machine with Application of Information Technology

2013 ◽  
Vol 859 ◽  
pp. 23-27 ◽  
Author(s):  
Zhi Feng Zhang ◽  
Cheng Gan ◽  
Xiao Jian Ding ◽  
Zeng Yu Cai
Keyword(s):  
Information Technology ◽  
Support Vector Machine ◽  
Extreme Learning Machine ◽  
Optimization Model ◽  
Optimization Method ◽  
Support Vector ◽  
Generalization Ability ◽  
Learning Parameter ◽  
Learning Machine

Based on the research of extreme learning machine and support vector machine, this paper does the research on the optimization method on extreme learning machine. This paper suggests an optimization model of extreme learning machine based on the improvement of the old model, and this model has obvious improvement on generalization ability and learning parameter ability. This approach can improve the development efficiency in the information technology, the experiment indicate this approach is efficient.

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

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