A novel algorithm with differential evolution and coral reef optimization for extreme learning machine training.

Extreme Learning Machine (ELM) is a learning algorithm proposed recently to train single hidden layer feed forward networks (SLFN). It has many attractive properties that include better generalization performance and very fast learning. ELM starts by assigning random values to the input weights and hidden biases and then in one step it determines the output weights using Moore-Penrose generalized inverse. Despite the aforementioned advantages, ELM performance might be affected by the random initialization of weights and biases or by the large generated network which might contain unnecessary number of neurons. In order to increase the generalization performance and to produce more compact networks, a hybrid model that combines ELM with competitive swarm optimizer (CSO) is proposed in this paper. The proposed model (CSONN-ELM) optimizes the weights and biases and dynamically determines the most appropriate number of neurons. To evaluate the effectiveness of the CSONN-ELM, it is experimented using 23 benchmark datasets, and compared to a set of static rules extracted from literature that are used to determine the number of neurons of SLFN. Moreover, it is compared to two dynamic methods that are used to enhance the performance of ELM, that are Optimally pruned ELM (OP-ELM) and metaheuristic based ELMs (Particle Swarm Optimization-ELM and Differential Evolution-ELM). The obtained results show that the proposed method enhances the generalization performance of ELM and overcomes the static and dynamic methods.

Extreme Learning Machine (ELM) is an extension of feed forward Neural Network model with extremely learning capacity and good generalization capabilities. Generally, the classification performance of ELM highly focused on three parameters such as the input weight matrix, the value of bias and the number of hidden neurons presented. ELM randomly chooses the input weights and bias values to minimize the training time. The arbitrary selection of biases and the input weight produce an unforeseen result which causes training error and also produces lesser prediction accuracy. For improving efficiency of ELM training, Ant Colony Optimization with continuous domain algorithm (ACO R ) used to find the optimum input weight and hidden bias values for ELM. With the unequal distribution of classes in imbalanced data sets, ELM algorithms tussle to find good accuracy. ELM algorithm doesn't acquire the mandatory information about the minority class to make an accurate classification. To deal the issues of ELM, in this paper the hybrid algorithms Weighted ELM and Weighted ELM with ACO R are proposed. The main objective of weighted ELM is that the related weight value is computed and assigned for each training sample to increase the classification rate. ACO R method is also integrated with the weighted ELM to find the optimum input weight and bias to maximize the accuracy of classification task. The comparative analysis has been accomplished over Hepatitis dataset. Further, the experimental results clearly revealed that Weighted ELM with ACO R is superior to other proposed methods.

In this paper, an integrated model based on efficient extreme learning machine (EELM) and differential evolution (DE) is proposed to predict chaotic time series. In the proposed model, a novel learning algorithm called EELM is presented and used to model the chaotic time series. The EELM inherits the basic idea of extreme learning machine (ELM) in training single hidden layer feedforward networks, but replaces the commonly used singular value decomposition with a reduced complete orthogonal decomposition to calculate the output weights, which can achieve a much faster learning speed than ELM. Moreover, in order to obtain a more accurate and more stable prediction performance for chaotic time series prediction, this model abandons the traditional two-stage modeling approach and adopts an integrated parameter selection strategy which employs a modified DE algorithm to optimize the phase space reconstruction parameters of chaotic time series and the model parameter of EELM simultaneously based on a hybrid validation criterion. Experimental results show that the proposed integrated prediction model can not only provide stable prediction performances with high efficiency but also achieve much more accurate prediction results than its counterparts for chaotic time series prediction.

The accurate separation of ECG signals has become crucial to identify heart diseases. Machine learning methods are widely used to separate ECG signals. The aim of this study was to obtain optimal number of hidden neurons of the Extreme Learning Machine (ELM) using the differential evolution algorithm (DEA) and increase the accuracy rate of ECG classification. In this study, a public database on PhysioNet was used for ECG signal classification. A deep feature method using convolutional neural network was used to extract the major features of the ECG samples. Then, a conventional ELM was applied to the ECG signals. Subsequently, the ECG signals with deep properties were shared with the MATLAB classifier toolbox (k-NN, SVM, Decision Trees). In addition, the ECG signals in the dataset were tested using the Genetic Algorithm Wavelet Kernel-ELM (GAWK-ELM). Finally, the DEA-ELM was improved for the determination of the number of hidden neurons. This study optimized the hidden neuron numbers of traditional ELM with DEA using deep learning capabilities in the feature extraction. The aim of was to maximize the best cost of the DEA and achieve the optimal number of hidden neurons in ELM. Accuracy (Acc), sensitivity (Se), specificity (Spe) and F-measure were used as the performance metrics for the classifier performances. The classification results were 80.60%, 81.50%, and 83.12% with SVM, ELM and DEA-ELM, respectively. Thus, the best classification scores were accomplished with an accuracy of 83.12% with the algorithm supported by the DEA.

To understand the change trend of Lake Taihu’s water quality indexes in advance and prevent the occurrence of pollution incidents such as blue algae, the ammonia nitrogen index forecasted model was studied by taking the water quality monitoring data of Nanquan in Wuxi City as an example. The chromaticity, odor and taste, pH, nitrite nitrogen, chloride, dissolved oxygen, and algae were determined as input variables by implementing linear regression analysis through SPSS software to establish the Extreme Learning Machine (ELM) model for forecasting ammonia nitrogen index. Aiming at the problem of ELM randomly generating weights and thresholds resulting in low forecast accuracy, the Particle Swarm Optimization (PSO) algorithm was adopted to optimize ELM parameters. In order to solve the situation that the PSO algorithm is easy to fall into local optimum, the Adaboost algorithm was introduced to train weak ELM predictors, which were later combined into a strong predictor for the forecast. The results represent that the Adaboost-PSO-ELM model’s forecast coefficient of determination (\({R}^{2}\)) and root mean square error (\(RMSE\)) for ammonia nitrogen are 0.955 mg/L and 0.023 mg/L, respectively. Compared with the two models of ELM and PSO-ELM, the forecast accuracy is significantly improved. Therefore, the ELM model based on Adaboost and PSO algorithms is feasible for forecasting water quality index of ammonia nitrogen in Lake Taihu, providing new ideas for accurate forecast in water quality aspect.KeywordsLake TaihuAmmonia nitrogenAdaboost-PSO-ELM

The Extreme Learning Machine (ELM) is a fast and straightforward learning algorithm designed for single hidden layer feed-forward neural networks (SLFNs). The ELM randomly assigns input weights and calculates the output weights analytically using the Moore-Penrose generalized inverse (MP) (also called Pseudo inverse). However, the MP is not preferable for ill-posed systems as it is sensitive to perturbation and may produce inaccurate solutions. Furthermore, it is computationally expensive as it involves matrix inversion. Regularized ELM (RELM) was proposed to address the limitations of the MP. In RELM, the regularization parameter is the primary factor that controls the classifier's performance. Though the RELM is stable and produces accurate results, finding a n a ppropriate regularization parameter value is tedious and increases the computation. This work proposes a variant of ELM named LSQR-ELM algorithm, which balances the ELM and RELM by maintaining stability and accuracy and avoiding the regularization entirely. The LSQR-ELM is based on the Least Squares QR (LSQR) iterative technique. Since the LSQR finds p a rtial Q R rather than full QR, it reduces unnecessary computation. To verify the proposed algorithm's ability, comparisons are made with several existing methods including ELM, Minimum Residual Method (MT-ELM), SVD-based SG-ELM, Optimally Pruned ELM (OP-ELM), and K-nearest neighbor (KNN). Extensive experimen-tation on various datasets demonstrates that the LSQR-ELM algorithm outperforms ELM, SG-ELM, OP-ELM, and KNN on most datasets. Moreover, it exhibits comparable performance to MT-ELM while requiring significantly f e wer computations.

Manifold learning in local tangent space via extreme learning machine

Structures with fixed-base will produce high accelerations and inter-storey drifts and move laterally during earthquake. The presence of base isolation devices between ground and the structure, reduces the structural vibrations. To maintain the seismic response of structures within safety, service and comfort limits, the combination of base isolators and feedback controllers have been proposed in recent years. This paper proposes a discrete direct adaptive extreme learning machine (ELM) controller for the active control of non linear base isolated building with hysteretic isolation system. The controller is constructed based on a single hidden layer feed forward network and the parameters of the network are adapted using extreme learning machine (ELM) algorithm. In this work, different from the original ELM algorithm the output weights of the network are updated using Lyapunov stability approach, to guarantee the stability of the structure. The performance of the proposed controller is verified on a non-linear three dimensional benchmark base-isolated structure by exciting the structure with three earthquake samples. The result shows that the proposed controller is effective in reducing the seismic responses of the isolation members as well as the superstructure.

Extreme learning machine (ELM) is a well-known algorithm for single layer feedforward neural networks (SLFNs) and their learning speed is faster than traditional gradient-based neural networks. However, many of the tasks that ELM focuses on are single-label, where an instance of the input set is associated with one label. This paper proposes a new method for training ELM that will be capable of multi-label classification using the Canonical Correlation Analysis (CCA). The new method is named CCA-ELM. There are 4 steps in the training process: the first step is to compute any correlations between the input features and the set of labels using CCA, the second step maps the input space and label space to the new space, the third step uses ELM to classify and the last step is to map to the original input space. The experimental results show that CCA-ELM can improve ELM for classification on multi-label learning and its recognition performances are better than the other comparative algorithms that use the same standard CCA.

Trends in extreme learning machines: A review

Extreme learning machine (ELM) was extended from the generalized single hidden layer feedforward networks where the input weights of the hidden layer nodes can be assigned randomly. It has been widely used for its much faster learning speed and less manual works. Considering the field of multi-label text classification, in this paper, we propose an ELM based algorithm combined with \(L_{21}\)-norm minimization of the output weights matrix called \(L_{21}\)-norm Minimization ELM, which not only fully inherits the merits of ELM but also facilitates group sparsity and reduces complexity of the learning model. Extensive experiments on several benchmark data sets show a more desirable performance compared with other common multi-label classification algorithms.

Extreme learning machine (ELM) is an easy-to use and effective learning algorithm of single-hidden layer feed-forward neural networks (SLFNs). The classical learning algorithm in neural network, e. g. backpropagation, requires setting several user-defined parameters and may get into local minimum. However, ELM only requires setting the number of hidden neurons and the activation function. It does not require adjusting the input weights and hidden layer biases during the implementation of the algorithm, and it produces only one optimal solution. Therefore, ELM has the advantages of fast learning speed and good generalization performance. In this paper, ELM is introduced in predicting reservoir permeability. By comparing to SVM, we analyze its feasibility and advantages in reservoir permeability prediction. The experimental results show that ELM has similar accuracy compared to SVR, but it has obvious advantages in parameter selection and learning speed.

Extreme Learning Machine (ELM) is a single hidden layer feed-forward neural network learning method, which has a high generalization performance as well as faster. In this paper, odour data is discriminated based on the sensor response curve by using ELM, and the main objective is to investigate the optimum number of nodes in the hidden layer of ELM for olfactory detection. The relationship between the number of nodes in the hidden layer and the number of attributes or classes of dataset is queried to achieve the goal. Three odour datasets taken from different sources in literature and two transfer functions for the ELM are used to verify the results of the study. The backpropagation (BP) algorithm is also used for training an artificial neural network for comparison purposes. The analysis is performed for the three datasets by using ELM and BP and obtained results present that the time consumption of ELM is too small to be compared with BP even though the number of nodes is high and better accuracy rates are obtained by ELM.

This paper develops a state-of-charge (SOC) estimation model for a lithium-ion battery using an improved extreme learning machine (ELM) algorithm. ELM is suitable for an SOC estimation since the ELM algorithm has fast estimation speed, good generalization performance, and high accuracy. However, the performance of ELM is highly dependent on training accuracy and the number of neurons in a hidden layer. Hence, a gravitational search algorithm (GSA) is applied to improve the ELM computational intelligence by searching for the optimal value hidden layer neurons. The optimal ELM-based GSA model does not require internal battery knowledge and mathematical model for an SOC estimation. The model robustness is validated at different temperatures using different electric vehicle drive cycles. The performance of the ELM-GSA model is verified with two popular neural network methods: back-propagation neural network (BPNN) and radial basis function neural network (RBFNN). The results are evaluated using different error rates and computation costs. The results demonstrate that the ELM-based GSA model offers a higher accuracy and lower SOC error rate than those of BPNN-based GSA and RBFNN-based GSA models. Furthermore, a detailed comparative study between the proposed model and existing SOC strategies is conducted, which also demonstrates the superiority of the proposed model.

Large-scale grid connection of green and clean energy has become a development trend in the energy field. Accurate prediction of wind power output power can reduce the negative impact of wind power on the power system. In this paper, we use extreme learning machine method to achieve short-term prediction of wind power, and determine the optimal number of hidden layer neurons by lattice method. In order to verify the effectiveness of extreme learning machine neural network, we compare the simulation results of extreme learning neural network method and BP neural network method. Simulation results show that the prediction accuracy of extreme learning machine method is similar to that of BP neural network. At the same, considering that extreme learning machine only need to calculate the weight matrix from the hidden layer to the output layer, so extreme learning machine has certain computing advantage compared with BP neural networks.