Deep Network Based on Stacked Orthogonal Convex Incremental ELM Autoencoders

Extreme learningmachine (ELM) as an emerging technology has recently attractedmany researchers’ interest due to its fast learning speed and state-of-the-art generalization ability in the implementation. Meanwhile, the incremental extreme learning machine (IELM) based on incremental learning algorithmwas proposed which outperformsmany popular learning algorithms. However, the incremental algorithms with ELM do not recalculate the output weights of all the existing nodes when a new node is added and cannot obtain the least-squares solution of output weight vectors. In this paper, we propose orthogonal convex incremental learning machine (OCI-ELM) with Gram-Schmidt orthogonalization method and Barron’s convex optimization learning method to solve the nonconvex optimization problem and least-squares solution problem, and thenwe give the rigorous proofs in theory.Moreover, in this paper, we propose a deep architecture based on stacked OCI-ELM autoencoders according to stacked generalization philosophy for solving large and complex data problems.The experimental results verifiedwith bothUCI datasets and large datasets demonstrate that the deep network based on stacked OCI-ELM autoencoders (DOC-IELM-AEs) outperforms the other methods mentioned in the paper with better performance on regression and classification problems.


Introduction
Extreme learning machine (ELM) proposed by Huang et al. [1,2] is a specific type of single-hidden layer feedforward network (SLFN) with randomly generated additive or RBF hidden nodes and hidden node parameters, which has recently been extensively studied by many researchers in various areas of scientific research and engineering due to the excellent approximation capability.Wang et al. presented ASLGEM-ELM algorithm, which provides some useful guidelines for improving the generalization ability of SLFNs trained with ELM [3].Alongside probing deeply into the research of theory and its application, ELM has become one of the leading trends for fast learning [4][5][6][7].Recently, Huang et al. [8] have proposed an algorithm called incremental extreme learning machine (I-ELM) which randomly adds nodes to the hidden layer one by one and freezes the output weights of the existing hidden nodes when a new hidden node is added [9][10][11][12].Then, Huang et al. [13] also showed its universal approximation capability for the case of fully complex hidden nodes.I-ELM is fully automatically implemented and in theory no intervention is required for the learning process from users.But there still exist some issues to be tackled [14]: (1) The redundant nodes can be generated in I-ELM, which have a minor effect on the outputs of the network.Moreover, the existence of redundant nodes can eventually increase the complexity of the network.
(2) The convergence rate of I-ELM is slower than ELM, and the number of hidden nodes in I-ELM is sometimes larger than the dimension of samples for the training.
In this paper, we propose a method called orthogonal convex extreme learning machine (OCI-ELM) to further settle the aforementioned problems of I-ELM.With the rigorous proofs in theory, we can obtain the least-squares solution of  =  and faster convergence rate by adopting the Gram-Schmidt orthogonalization method incorporated into CI-ELM [15].The simulations on real-world datasets show that the proposed OCI-ELM algorithm can achieve faster convergence rates, more compact neural network, and 2 Mathematical Problems in Engineering better generalization performance than both I-ELM and the improved I-ELM algorithms while keeping the simplicity and efficiency of ELM.
Recently, deep learning has attracted many research interests with its remarkable success in many applications [16][17][18].Deep learning is an artificial neural network learning algorithm which has multilayer perceptrons.Deep learning has achieved an approximation of complex functions and alleviated the optimization difficulty associated with the deep models [19][20][21].Motivated by the remarkable success of deep learning [22,23], we propose a new stacked architecture to solve large and complex data problems using OCI-ELM autoencoder as the training algorithm in each layer, which incorporates the excellent performance of OCI-ELM with the ability of complex function approximation derived from deep architectures.We implemented OCI-ELM autoencoder in each iteration of deep orthogonal convex incremental extreme machine (DOC-IELM) to reconstruct the input data and estimate the errors of the prediction functions with the scheme of layer-by-layer architectures.Both the supervised and the unsupervised data all can be the pertaining input of the new proposed deep network.Moreover, the OCI-ELM autoencoder-based deep network (DOC-IELM-AEs) can suffice to achieve the efficiency improvement for generalization performance.
To show the effectiveness of DOC-IELM-AEs, we apply it to both the ordinary real-world datasets with UCI datasets and large datasets with MNIST, OCR Letters, NORB, and USPS datasets.The simulations show that the proposed deep model possesses better accuracy of testing and more compact network architecture than the aforementioned improved I-ELM and other deep models without incurring the out-ofmemory problem.
This paper is organized as follows.Section 2 reviews the preliminary knowledge of incremental extreme learning machine (I-ELM).Section 3 describes OCI-ELM algorithm, the proposed model which adopts the Gram-Schmidt orthogonalization method into convex I-ELM (CI-ELM).Section 4 makes a comparison between OCI-ELM and other algorithms.Section 5 presents the details of DOC-IELM-AEs algorithm and compares the performance with deep architecture models.Section 6 applies the DOC-IELM-AEs algorithm into elongation prediction of strips.Finally, Section 7 concludes this paper.

Related Works
In this section, the main concepts and theory of the I-ELM [8] algorithm are shortly reviewed.For the sake of generality, we assume that the network has only one linear output node, and all the analysis can be easily extended into multinonlinear output nodes cases.Consider a training dataset ℵ = {(  ,   ) |   ∈   ,   ∈   , 1 ≤  ≤ }; the SLFN with  additive hidden nodes and activation function () can be represented by where   = [ 1 ,  2 , . . .,   ]  is the weight vector connecting the input layer to the th hidden node,   = [ 1 ,  2 , . . .,   ]  is the weight connecting the th hidden node to the output node,   is the threshold of the th hidden node, and  is the hidden node activation function.
The I-ELM proposed by Huang et al. is different from the conventional ELM algorithm; I-ELM is an automatic algorithm which can randomly add hidden nodes to the network one by one and freeze all the weights of the existing hidden nodes when a new hidden node is added, until the expected learning accuracy is obtained or the maximum number of hidden nodes is reached.Thus, I-ELM algorithm can be summarized in Algorithm 1.
Step 2 (learning step).While  <  max , ‖‖ > , (a) increase the number of hidden nodes by one; (b) assign random input weight  and bias  for hidden nodes ; (c) calculate the residual error after adding the new hidden node; (d) calculate the output weight  for the new hidden nodes:  = ⟨,   ⟩/‖  ‖ 2 ; (e) calculate the residual error:  =  −  ⋅   ; Endwhile.

The Proposed Orthogonal Convex Incremental Extreme Learning Machine (OCI-ELM)
The motivation for the work in this section comes from the important properties of basic ELM as follows: (1) The special solution β =  †  is one of the leastsquares solutions of a general linear system  = , meaning that the smallest training error can be reached by this special solution: ‖ β−‖ = ‖ † − ‖ = min  ‖ − ‖. ( (3) The minimum norm least-squares solution of  =  is unique, which is β =  † .
In this section, we propose an improved I-ELM algorithm (OCI-ELM) based on Gram-Schmidt orthogonalization method combined with Barron's convex optimization learning method and prove the OCI-ELM algorithm in theory which can obtain the least-squares solution of  = .Meanwhile, OCI-ELM can achieve a more compact network architecture, faster convergence rate, and better generalization performance than other improved I-ELM algorithms while retaining the I-ELM's simplicity and efficiency.[24].Given a linearly independent vector set [ 1 ,  2 , . . .,   ] in the inner product space  ⊆   , the vector set  1 ,  2 , . . .,   for Gram-Schmidt orthogonalization process is as follows [25]:

Theorem 2. Gram-Schmidt orthogonalization process converts linearly independent vectors into orthogonal vectors
where  1 , Proof.Given the vector set V 1 , V 2 , . . ., V −1 and vector   , suppose there exist scalars  1 ,  2 , . . .,  −1 ; then the linear combination of those vectors with those scalars as coefficients is Substituting (4) into (5), we have V  = 0: CI-ELM was originally proposed by Huang and Chen [15], which incorporates Barron's convex optimization learning method into I-ELM.By recalculating the output weights of the existing hidden nodes randomly generated after a new node is added, the CI-ELM can obtain better performance than I-ELM.Incorporated with Gram-Schmidt orthogonalization and Barron's convex optimization learning method, the process of OCI-ELM algorithm can be described in Algorithm 4.
Step 2. This step consists of two steps as follows.

Orthogonalization
Step.In this step, the following is carried out: (a) Increase the number of hidden nodes  and  by one, respectively:  =  + 1 and  =  + 1.
(b) Randomly assign hidden node parameters (  ,   ) for new hidden node  and calculate the output   , and the hidden layer output matrix , Learning Step.While  1 <  max , ‖‖ > , (c) calculate the output weight   for the newly added hidden node:  (a) According to the condition given above, we have the following: (1) Here, (2) When the output weight (3) When the output weight we also have suppose that, for all  ≤ , we have (5) When the output weight So, (b) According to (17), we have And

Experiments and Analysis
In this section, we tested the generalization performance of the proposed OCI-ELM with other similar learning algorithms on ten UCI real-world datasets, including five regression and five classification problems, as shown in Table 1.The The experimental results between OCI-ELM and some other ELM algorithms on regression and classification problems are given in Tables 2 and 3.In Tables 2 and 3, the best results obtained by the OCI-ELM and the other 4 algorithms are italicized and shown in boldface.In Section 4.1, we compare the generalization performance of OCI-ELM with another six state-of-the-art algorithms on regression problems.In Section 4.2, we compare the generalization performance of OCI-ELM with the same six algorithms on classification problems.All these results in this section are obtained from thirty trials for all cases, and the mean results (mean), root-mean-square errors (RMSE), and standard deviations (Std.) are listed in the corresponding tables, respectively.The seven representative evolutionary algorithms are listed as follows: (i) Convex incremental extreme learning machine (CI-ELM) [15].

Mathematical Problems in Engineering
In short, OCI-ELM can generally achieve better performance on these regression and classification problems in terms of training (and testing) RMSE for regression and testing accuracy for classification.Moreover, the compactness of network and convergence rate also display the good performance of OCI-ELM algorithm.

Deep Network Based on Stacked OCI-ELM
Autoencoders (DOC-IELM-AEs) 5.1.OCI-ELM Autoencoder.As an artificial neural network model, the autoencoder is frequently applied in the deep architecture approaches.Autoencoder is a kind of unsupervised neural network, where the input of network is equal to the output.Kasun et al. [29] proposed an autoencoder based on ELM (ELM-AE).According to their ELM-AE theory, the model of ELM-AE is composed of input layer, hidden layer, and output layer.In addition, the weights and biases of the hidden nodes are randomly generated via orthogonalization, and the input data is projected to a different or equal dimension space [30]; the expressions are as follows: where  = [ 1 , . . .,   ]  are the weights generated orthogonally randomly and  = [ 1 , . . .,   ]  are the biases generated orthogonally randomly between the input and hidden nodes.
There are three calculation approaches to obtain the output weight  of ELM-AE: (1) For sparse ELM-AE representations, output weights  can be calculated as follows: (2) For compressed ELM-AE representations, output weights  can be calculated as follows: (3) For equal dimension ELM-AE representations, output weights  can be calculated as follows: In this section, we present the OCI-ELM, which is incorporated with Barron's convex optimization learning method and Gram-Schmidt orthogonalization method into I-ELM to achieve the optimal least-squares solution as the training algorithm for an autoencoder instead of conventional autoencoders, which apply backpropagation algorithm (BP) for training to obtain the identity function and normal ELM for training the autoencoder.Because of the adoption of incremental algorithm, there is no need to set the number of hidden nodes according to the experience.With the initialization of the maximum value  max of hidden nodes the, number of hidden nodes can be increased by more than one node each time, until the stop criterions are met; for example, residual error  is equal to the expected learning accuracy  or the number of hidden nodes  achieves  max .

Implementation of
If ‖E‖ = : number of iterations  max , and expected learning accuracy , one has the following.
Step 2 (orthogonal convex I-ELM autoencoder on layer 1).This step consists of two steps as follows.

Orthogonalization
Step.In this step, the following is carried out: (a) Increase the number of hidden nodes  1 and  1 by one, respectively: (b) Randomly assign hidden node parameters (  ,   ) for new hidden node  and calculate the output   , and the hidden layer output matrix, Learning Step.While  1 <  max , ‖‖ > , (c) calculate the output weight  1() for the newly added hidden node: (d) recalculate the output weight vectors of all existing hidden nodes if  1 > 1: (e) calculate the residual error after adding the new hidden node  1 : Step 3 (orthogonal convex I-ELM autoencoder on layer 2 → Ñ).This step is carried out as follows.

Learning
Step.While  2→ Ñ <  max , ‖‖ > , (a) calculate the output weight  1() for the newly added hidden node with the hidden layer output matrix : Endwhile.
The DOC-IELM-AEs algorithm inherits the advantages of incremental constructive feedforward networks model and deep learning algorithms on exactly capturing higherlevel abstractions and characterizing the data representations.The implementation of autoencoders for the unsupervised pretraining of data exhibits super-duper performance on regression and classification problems.The improved method utilizes the OCI-ELM-AE as a base building layer to construct the whole deep architecture.As shown in Figure 2, the data is mapped to OCI-ELM feature space; in each layer, OCI-ELM-AE output weights with respect to input data are the weight of the first layer; for the same reason, the output weights of OCI-ELM-AE, with respect to hidden layer output, are the layer weights of DOC-IELM-AEs.The detailed algorithm of DOC-IELM-AEs is shown in Algorithm 6.

Input layer
Multi-hidden layers Output layer

Orthogonal convex I-ELM autoencoder
Convex incremental extreme machine  The regression performance comparisons of the proposed algorithms OCI-ELM and DOC-IELM-AEs with the baseline methods including SVM [35], single ELM, ML-ELM, AE-S-ELMs, DBN, ErrCor [36], and PC-ELM are shown in Table 5.The specific analyses on the results of regression capability and effectiveness are as follows: (1) OCI-ELM compared with SVM, ELM, ErrCor, and PC-ELM: we perform the regression testing on the datasets described in Table 4.The simulations are obtained by the average of 50 trails; we can observe from Table 4 that the testing accuracies of OCI-ELM on UCI datasets and large datasets are both better than SVM, ELM, ErrCor, and PC-ELM.For BlogFeedback dataset, the training accuracy of OCI-ELM is 91.76%, and those of SVM, ELM, ErrCor, and PC-ELM are 89.75%,90.12%, 90.39%, and 90.54%, respectively.Meanwhile, OCI-ELM also obtains better testing accuracy of 91.82% than other algorithms.Although the OCI-ELM is an iterative learning algorithm, the compact neural network makes the convergence rate faster than PC-ELM and ErrCor, merely slower than SVM and ELM.Thus, the training time consumed on OCI-ELM learning is acceptable.
(2) DOC-IELM-AEs compared with DBN, ML-ELM, and AE-S-ELMs: the testing accuracy on UCI datasets can show that the performance of DOC-IELM-AEs outperforms the OCI-ELM.With the aforementioned comparisons between OCI-ELM and the other algorithms (SVM, ELM, ErrCor, and PC-ELM), evidenced by the same token, the DOC-IELM-AEs can achieve better testing accuracy than SVM, ELM, ErrCor, and PC-ELM; this result can also be seen in Table 5.For the large-scale datasets (BlogFeedback and Online News Popularity), the DOC-IELM-AEs obtained accuracies of 93.16%, 93.27% and 93.69%, 93.84% for training and testing with the network structures 281-1000-1000-2000-10 and 61-700-700-10000-26, respectively.The simulations in Table 5 show that DOC-IELM-AEs can produce better results than DBN, ML-ELM, and AE-S-ELMs.Furthermore, DOC-IELM-AEs enjoys the advantage over the DBN and ML-ELM on training speed.Thus, with the better regression performance, DOC-IELM-AEs would provide the state-of-the-art method for large-scale unstructured data problems.

Performance Comparison of Classification Problems
Based on DOC-IELM-AEs.The classification performance  6.The specific comparisons are as follows: (1) OCI-ELM compared with SVM, ELM, ErrCor, and PC-ELM: the simulation results are obtained by the average of 50 trails on datasets in Table 4 (from Delta Ailerons to NORB data).For the BlogFeedback, the training and testing accuracies are 91.76% and 91.82%, respectively, listed in Table 6; we can see that OCI-ELM achieves better classification accuracy than SVM, ELM, ErrCor, and PC-ELM.And the speed of learning is faster than other improved ELM algorithms, notwithstanding behind the SVM and single ELM due to the process of iteration learning.
(2) DOC-IELM-AEs compared with DBN, ML-ELM, and AE-S-ELMs: to test these anticipated effects, we used UCI datasets and large-scale datasets to acquire the results.From the experimental results, we can see that the classification accuracies of DOC-IELM-AEs are better than others obviously.Focusing on NORB, the network structure used by DOC-IELM-AEs is 2048-800-800-3000-5; the DOC-IELM-AEs obtained the best accuracies of 93.16%, 93.27% and 93.69%, 93.84% for training and testing, respectively, in all algorithms, including SVM, single ELM, ML-ELM, AE-S-ELMs, DBN, ErrCor, PC-ELM OCI-ELM, and DOC-IELM-AEs.Furthermore, the simulation results of other datasets also display the outstanding performance of DOC-IELM-AEs.Thus, with the better accuracy and faster speed of training, DOC-IELM-AEs can be applied in the vast majority of classification problems.

Case Study on Elongation Prediction of Strips
In this section, all of the experimental results for the elongation of strips prediction are presented.The annealing treatment is considered the most important process to cold rolled strips.In this process, the cold working hardening and internal stress of strips can be eliminated; the hardness of strips can be reduced; moreover, the ability of plastic deformation, stamping, and mechanical technique can be improved.Figure 3 shows the process of continuous annealing.In the furnace, the strips will pass five temperature sections, that is, preheating section (PHS), heating section (HS), slow cooling section (SS), rapid cooling section (RCS), and equalising section (ES), and three tension sections, that is, SS tension section, RCS tension section, and HS tension section.Therefore, the strips will extend or shorten with the changes of temperature and tension.Meanwhile, the surface friction coefficient and the rotational speed of the tension rolls also affect the elongation of strips, rendering the weld position unable to be tracked accurately, having a great influence on the rate of finished product and the safety of air-knife.Thus, the proposed method DOC-IELM-AEs is applied in the annealing of strips process to obtain the position information of welds.The annealing process has 12 continuous process measurements and 10 manipulated variables according to the experience and mechanism analysis.We collect the historical records in the last 16 months which can affect the position of the welding seam, including the temperature data of 5 sections, the tension data of 3 sections, and the speed data of 11 sections.We use data of 10 months for training and the following data of 6 months for testing.The comparison results of elongation of strips prediction are shown in Figure 4. From the figures, we can see that the prediction results in the 6 months obtained based on 6 algorithms can all approximate the measured values.Although there are only little differences in the experimental results, the prediction based on DOC-IELM-AEs consistently outperforms the other methods in the comparisons.For further investigation on the prediction capabilities of DOC-IELM-AEs, the performances of algorithms are evaluated in terms of four criteria, that is, the mean absolute percentage error (MAPE), the mean square error (MSE), the relative root-mean-square error (rRMSE), and the absolute fraction of variance ( 2 ).During testing of DOC-IELM-AEs and other algorithms, they are also defined by using the following equations: where   and    are measured value and predicted value, respectively, and  is the number of testing data.The smaller MAPE, MSE, and rRMSE and the larger  2 are indicative of better generalization performance of algorithm.
MAPE in Figure 5(a) evaluates the effect on measured values, generated by the disparity between the elongation values of steel-strips and the predicted values.Meanwhile, MSE and rRMSE in Figures 5(b) and 5(c), respectively, also reflect the dispersion of models, where MSE are more sensitive to the large errors compared with rRMSE because the squared errors amplify the large errors further. 2 in Figure 5(d) is the distance between errors and predicted values. 2 closer to 1 means that the algorithms have better performances.By analyzing the comparisons, it has become apparent that the results for evaluation criteria based on arbitrary 6-month testing data show better generalization performances of DOC-IELM-AEs than other algorithms in experiments of comparison.Accordingly, there is important practical significance in the prediction of elongation of steelstrips using DOC-IELM-AEs.
In order to demonstrate the effectiveness of the algorithm proposed in practical engineering, we have selected the successive data of 12 months from the whole data (16-month data) to conduct the comparisons, and we obtained the prediction accuracies of every month and one year.The comparisons of prediction accuracy shown in Figure 6 indicate that the performance of the algorithm proposed is the best overall, with mean accuracy of 96.795% in 12 months (all the year), compared with those of 92.49%, 92.71%, 94.47%, 94.62%, 94.43%, 93.18%, 93.22%, and 94.50% obtained from SVM, ELM, ML-ELM, AE-S-ELMs, DBN, ErrCor, PC-ELM, and OCI-ELM methods, respectively.DOC-IELM-AEs has the best accuracy among the nine methods, which indicates the predictive stability and performance of the method.Therefore, we can get the conclusion that DOC-IELM-AEs has the best prediction performance in the testing, and the algorithm proposed is a very effective method.

Conclusions
In this paper, we proposed a stacked architecture with OCI-ELM algorithm based on deep representation learning and added the OCI-ELM autoencoder into each layer of OCI-ELM, called DOC-IELM-AEs.The experiment results have demonstrated strongly that DOC-IELM-AEs can be suitable for solving regression and classification problems; simulations showed that, (1) compared with CI-ELM, EI-ELM, ECI-ELM, PC-ELM, and OCI-ELM, DOC-IELM-AEs can achieve the best testing accuracy with the same network size, even less hidden nodes; meanwhile, the speed of learning is also faster than other algorithms.Moreover, DOC-IELM-AEs has better performance than OCI-ELM algorithm; (2) compared with SVM, ELM, ML-ELM, AE-S-ELMs, DBN, ErrCor, PC-ELM, and OCI-ELM, DOC-IELM-AEs can also obtain the best testing accuracy with consuming more time in a certain range for the large datasets; (3) compared with SVM, ELM, ML-ELM, AE-S-ELMs, DBN, ErrCor, PC-ELM, and OCI-ELM, the DOC-IELM-AEs applied in the case of strips-elongation prediction can enhance the performance of prediction; demonstrated with the production data, the prediction accuracy based on the algorithm we proposed outperforms other algorithms.For these reasons, the OCI-ELM and DOC-IELM-AEs can further be implemented in practical engineering and have the potential for solving more complicated big data problems with further study.

Figure 3 :
Figure 3: General scheme of strip steel in annealing process.

Figure 4 :
Figure 4: The comparisons of prediction on 6-month strip-elongation data.

Figure 5 :
Figure 5: Different prediction errors of 5 models on 6-month data.

Figure 6 :
Figure 6: The comparisons of prediction accuracy of annual statistical data.

Table 3
RMSE of the training and testing with fixed hidden nodes obtained from OCI-ELM and another six algorithms, respectively.Meanwhile, hidden nodes and learning time with the same stop RMSE are also shown in Table 2.For California Housing dataset in the table, the OCI-ELM provides lower training and testing RMSE rate (0.1272 and 0.1263) than 9766; and 192.33 and 0.9713.Where SB-ELM is the fixed ELM, thus there is a difficulty in finding the accurate stop criterion.Likewise, in the CCS dataset, the hidden node of SB-ELM is an approximate value; meanwhile, OCI-ELM shows better generalization performance than other algorithms in comparisons.Although, in the cases of Auto MPG, Servo, and Parkinsons, the learning time consumed by OCI-ELM shows that the presented algorithm is not the top spot, the average convergence rate of five regression problems consumed by OCI-ELM is still the fastest.Moreover, the average convergence rate demonstrates that the stability performance of OCI-ELM is better than other algorithms.The proposed algorithm can retain the simplicity and efficiency of incremental ELM and obtain the leastsquares solution of  =  by incorporating the Gram-Schmidt orthogonalization method.The optimal solution obtained from  =  means that the best hidden node parameter leading to the largest residual error decreasing will be added to the existing network.Therefore, OCI-ELM can efficiently reduce the network complexity and meanwhile enhance the generalization performance of the algorithm., the results of comparisons obtained from OCI-ELM are better than those of the other algorithms.For Waveform II dataset in the table, the OCI-ELM proposed displays better training accuracy and standard deviation of 93.11% and 0.0083 than CI-ELM (84.47% and 0.0182), PC-ELM (89.81% and 0.0104), LOO-IELM (88.93% and 0.0097), SB-ELM (80.69% and 0.0181), II-RELM (90.64%

Table 2 :
The comparisons of training and testing on the regression cases.
and 0.0112), and EIR-ELM (91.15% and 0.0096), thanks to the better classification ability of OCI-ELM.In addition, the hidden nodes 29.54 and the average time 3.0864 s are also less than others, which can demonstrate that OCI-ELM has more reasonable network structure than CI-ELM, PC-ELM, LOO-IELM, SB-ELM, II-RELM, and EIR-ELM, which efficiently reduces the complexity of the network.Although SB-ELM shows the advantages in training speed as the fixed ELM, OCI-ELM generally produces better performance in comprehensive consideration of accuracy and speed for practical problems which need higher accuracy demand.

Table 3 :
The comparisons of training and testing on the classification cases.

Table 5 :
The comparisons of training and testing on the regression cases.

Table 6 :
The comparisons of training and testing on the classification cases.