Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine

Mao, Yachun; Le, Ba Tuan; Xiao, Dong; He, Dakuo; Liu, Chongmin; Jiang, Longqiang; Yu, Zhichao; Yang, Fubin; Liu, Xinxin

doi:10.1016/j.optlastec.2019.01.005

Cited by 30 publications

(12 citation statements)

References 31 publications

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“…In order to investigate the improvement of learning accuracy of our methods, original ELM [5], TELM [17], and MRELM [18] are also evaluated. All the [27,28]. To evaluate the robustness of our FC-IMRELM algorithm, we conducted the tests using simple benchmark datasets and real datasets collected from coal and iron ores industries.…”

Section: Resultsmentioning

confidence: 99%

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Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

Liu

2019

Mathematical Problems in Engineering

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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.

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Section: Resultsmentioning

confidence: 99%

“…where = 1, 2, ⋅ ⋅ ⋅ , , = 1, 2, ⋅ ⋅ ⋅ , . Substitute equation (27) into equation (25) and multiply both sides of the equation by ( ) −1 :…”

Section: Process Of Matrices Decomposition For Fc-imrelmmentioning

confidence: 99%

Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

Liu

2019

Mathematical Problems in Engineering

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“…Our experiment is divided into two parts. e first part is a simple benchmark classification [26], and the second part is to classify coal by using the spectral information of coal [27]. In addition, in order to comprehensively evaluate the performance of the P-ELM algorithm and P-MELM algorithm, the initial value of the number of nodes in a single hidden layer is set as 100 in the experiment, and the step size is set as 1 to delete redundant nodes.…”

Section: Algorithm Verificationmentioning

confidence: 99%

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Liu

et al. 2021

Mathematical Problems in Engineering

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Extreme learning machine is originally proposed for the learning of the single hidden layer feedforward neural network to overcome the challenges faced by the backpropagation (BP) learning algorithm and its variants. Recent studies show that ELM can be extended to the multilayered feedforward neural network in which the hidden node could be a subnetwork of nodes or a combination of other hidden nodes. Although the ELM algorithm with multiple hidden layers shows stronger nonlinear expression ability and stability in both theoretical and experimental results than the ELM algorithm with the single hidden layer, with the deepening of the network structure, the problem of parameter optimization is also highlighted, which usually requires more time for model selection and increases the computational complexity. This paper uses Cholesky factorization strategy and Givens rotation transformation to choose the hidden nodes of MELM and obtains the number of nodes more suitable for the network. First, the initial network has a large number of hidden nodes and then uses the idea of ridge regression to prune the nodes. Finally, a complete neural network can be obtained. Therefore, the ELM algorithm eliminates the need to manually set nodes and achieves complete automation. By using information from the previous generation’s connection weight matrix, it can be evitable to re-calculate the weight matrix in the network simplification process. As in the matrix factorization methods, the Cholesky factorization factor is calculated by Givens rotation transform to achieve the fast decreasing update of the current connection weight matrix, thus ensuring the numerical stability and high efficiency of the pruning process. Empirical studies on several commonly used classification benchmark problems and the real datasets collected from coal industry show that compared with the traditional ELM algorithm, the pruning multilayered ELM algorithm proposed in this paper can find the optimal number of hidden nodes automatically and has better generalization performance.

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“…The model runs rapidly and has a simple structure, and a lot of scholars have applied and improved it. − In spectroscopy, the ELM is extensively applied in the construction of analytical and classification models. Mao et al established a multilayer ELM model for coal identification, and the experimental results show that it can achieve an identification accuracy of 92.25% while taking into account the speed. Yan et al proposed a method combining the kernel-based ELM (K-ELM) with LIBS for detecting carbon and sulfur content in coal and finally achieved an R2 of 0.994 and an RMSE of 0.3762%.…”

Section: Introductionmentioning

confidence: 99%

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Xiao

Yan

et al. 2022

ACS Omega

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Coal plays an indispensable role in the world’s energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.

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Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine

Cited by 30 publications

References 31 publications

Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

Incremental Multiple Hidden Layers Regularized Extreme Learning Machine Based on Forced Positive‐Definite Cholesky Factorization

Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

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