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 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.

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 Fabric Defect Detection and Classifier via Multi-Scale Dictionary Learning and an Adaptive Differential Evolution Optimized Regularization Extreme Learning Machine

2019 ◽  
Vol 27 (1(133)) ◽  
pp. 67-77 ◽  
Author(s):  
Zhiyu Zhou ◽  
Chao Wang ◽  
Xu Gao ◽  
Zefei Zhu ◽  
Xudong Hu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Dictionary Learning ◽  
Gabor Filter ◽  
Differential Evolution Algorithm ◽  
Adaptive Mutation ◽  
Multi Scale ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Learning Machine

To develop an automatic detection and classifier model for fabric defects, a novel detection and classifier technique based on multi-scale dictionary learning and the adaptive differential evolution algorithm optimised regularisation extreme learning machine (ADE-RELM) is proposed. Firstly in order to speed up dictionary updating under the condition of guaranteeing dictionary sparseness, k-means singular value decomposition (KSVD) dictionary learning is used. Then multi-scale KSVD dictionary learning is presented to extract texture features of textile images more accurately. Finally a unique ADE-RELM is designed to build a defect classifier model. In the training ADE-RELM classifier stage, a self-adaptive mutation operator is used to solve the parameter setting problem of the original differential evolution algorithm, then the adaptive differential evolution algorithm is utilised to calculate the optimal input weights and hidden bias of RELM. The method proposed is committed to detecting common defects like broken warp, broken weft, oil, and the declining warp of grey-level and pure colour fabrics. Experimental results show that compared with the traditional Gabor filter method, morphological operation and local binary pattern, the method proposed in this paper can locate defects precisely and achieve high detection efficiency.

Extreme learning machine for multivariate reservoir characterization

2021 ◽  
Vol 205 ◽  
pp. 108869
Author(s):  
Xingye Liu ◽  
Qiang Ge ◽  
Xiaohong Chen ◽  
Jingye Li ◽  
Yangkang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Reservoir Characterization ◽  
Learning Machine

scholarly journals Protein Sequence Classification with Improved Extreme Learning Machine Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jiuwen Cao ◽  
Lianglin Xiong
Keyword(s):  
Extreme Learning Machine ◽  
Protein Sequence ◽  
Protein Sequences ◽  
Activation Function ◽  
Majority Voting ◽  
Training Algorithms ◽  
Sequence Classification ◽  
Protein Sequence Classification ◽  
Learning Machine ◽  
Majority Voting Method

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images

2014 ◽  
Vol 11 (6) ◽  
pp. 1066-1070 ◽  
Author(s):  
Yakoub Bazi ◽  
Naif Alajlan ◽  
Farid Melgani ◽  
Haikel AlHichri ◽  
Salim Malek ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Hyperspectral Images ◽  
Learning Machine

scholarly journals Occupancy Prediction Using Differential Evolution Online Sequential Extreme Learning Machine Model

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4033
Author(s):  
Jonas Bielskus ◽  
Violeta Motuzienė ◽  
Tatjana Vilutienė ◽  
Audrius Indriulionis
Keyword(s):  
Energy Efficiency ◽  
Differential Evolution ◽  
Extreme Learning Machine ◽  
Open Space ◽  
Prediction Models ◽  
Co2 Concentration ◽  
Full Potential ◽  
Machine Model ◽  
Learning Machine ◽  
Occupancy Prediction

Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

Research on Optimization Technology of Facility Layout Based on Improved Differential Evolution Method

2014 ◽  
Vol 912-914 ◽  
pp. 1706-1709
Author(s):  
Ping Bo Qu
Keyword(s):  
Differential Evolution ◽  
Quadratic Assignment Problem ◽  
Facility Layout ◽  
Differential Evolution Algorithm ◽  
Layout Design ◽  
Quadratic Assignment ◽  
Problem Model ◽  
Facility Layout Design ◽  
Facility Logistics ◽  
The Cost

The facility layout design is the key problem in manufacturing system. Based on the constraints such as the cost of facility logistics and the space of equipment, this paper sets up a Quadratic Assignment Problem model of facility layout. The model is solved using Differential Evolution algorithm according to the features of facility layout, which is combined with Random Key technology. The test results performed on the liner and circular layout show the proposed approach can solve effectively the facility layout design problem.

Modeling of Drying Process of Bittim Nuts (pistacia terebinthus) in a Fixed Bed Dryer System by Using Extreme Learning Machine

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Asım Balbay ◽  
Engin Avci ◽  
Ömer Şahin ◽  
Resul Coteli
Keyword(s):  
Extreme Learning Machine ◽  
Fixed Bed ◽  
Experimental Studies ◽  
Learning Rule ◽  
Activation Function ◽  
Optimal Topology ◽  
Gradient Based ◽  
Learning Machine ◽  
Pistacia Terebinthus ◽  
Hidden Neurons

Abstract Artificial neural networks (ANNs) have been widely used in modeling of various systems. Training of ANNs is commonly performed by backpropagation based on a gradient-based learning rule. However, it is well-known that such learning rule has several shortcomings such as slow convergence and training failures. This paper proposes a modeling technique based on Extreme Learning Machine (ELM) eliminating disadvantages of backpropagation based on a gradient-based learning rule for the drying of bittim (pistacia terebinthus). The samples for ELM based model are obtained by experimental studies. In experimental studies, the sample mass loss rate as a function time was investigated in different air velocities (0.5 and 1 m/s) and air temperatures (40, 60 and 80°C) in a designed dryer system. The obtained samples from experiments are used for training and testing of ELM. Further, some parameters of ELM such as type of activation function and the number of hidden neurons are set to obtain the best possible modelling results. The obtained prediction results show that ELM algorithm with tangent sigmoid activation function and 20 hidden neurons is appeared to be most optimal topology since maximum R2 and minimum rms (0.0500) and cov (0.2256) values are obtained. Thus, it is concluded that ELM can be used as an effective modelling tool in the drying of bittim (pistacia terebinthus) in fixed bed dryer system.

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