Kernel-based Extreme Learning Machine Research Articles

Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning

Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error(RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.

NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification.

Spatial-spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics via spatial filtering, they lack the ability to consider correlations in non-Euclidean spaces. To address the aforementioned issues, we develop a new semisupervised HSI classification approach based on normalized spectral clustering with kernel-based learning (NSCKL), which can aggregate local-to-global correlations to achieve a distinguishable embedding to improve HSI classification performance. In this work, we propose a normalized spectral clustering (NSC) scheme that can learn new features under a manifold assumption. Specifically, we first design a kernel-based iterative filter (KIF) to establish vertices of the undirected graph, aiming to assign initial connections to the nodes associated with pixels. The NSC first gathers local correlations in the Euclidean space and then captures global correlations in the manifold. Even though homogeneous pixels are distributed in noncontiguous regions, our NSC can still aggregate correlations to generate new (clustered) features. Finally, the clustered features and a kernel-based extreme learning machine (KELM) are employed to achieve the semisupervised classification. The effectiveness of our NSCKL is evaluated by using several HSIs. When compared with other state-of-the-art (SOTA) classification approaches, our newly proposed NSCKL demonstrates very competitive performance. The codes will be available at https://github.com/yuanchaosu/TCYB-nsckl.

Model predictive control of mixing controlled compression ignition operation for low reactivity fuels

Using gasoline or other low reactivity fuels with a pilot injection or port fuel injection in a compression ignition engine has shown great potential in reducing NOx emissions while keeping high thermal efficiency compared to diesel. However, excessive combustion noise is caused by a high maximum pressure rise rate in the cylinder due to the higher fractions of premixed charge of the low-reactivity fuel. This noise can result in structural damage to engine components and as such, combustion noise limits the range of the operating parameters and makes the control of such engines challenging. In this study, a simulation environment was built up in MATLAB/Simulink leveraging a physics-based zero-dimension combustion model to capture the in-cylinder pressure time traces as well as metrics relevant to thermal efficiency and combustion noise. In order to also facilitate the control of emissions, machine learning models were investigated to capture NOx emissions. A kernel-based extreme learning machine (K-ELM) performed best and had a coefficient of correlation (R-squared) of 0.998. The combustion and NOx emission models are valid for not only conventional gasoline fuel but also oxygenated alternative fuel blends at three different pilot injection strategies. In order to track key combustion metrics while keeping noise and emissions within constraints, a model predictive control (MPC) was applied for a compression ignition engine operating with a range of potential fuels and fuel injection strategies. The MPC is validated under different scenarios, including a load step change, fuel type change, and injection strategy change, with proportional–integral (PI) control as the baseline. The simulation results show that MPC reduces about 26% of ringing intensity in the transient process and 17% at the steady state for E30. Generally, MPC can optimize the overall performance through modifying the main injection timing, pilot fuel mass, and exhaust gas recirculation (EGR) fraction.

Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.

Identification of procymidone in rapeseed oils based on olfactory visualization technology

A new method for qualitative identification of pesticide residues in edible oil based on olfactory visualization technology is proposed. Firstly, according to the pre-experimental experience, 12 suitable chemical dyes were selected to prepare sensor arrays to form an olfactory visualization system. The system was used to gather volatile odor information in rapeseed oil samples that had different levels of procymidone residues. Subsequently, the olfactory information was translated into a visual representation. Then, the visual data, i.e. color features, were preprocessed and underwent principal component analysis to extract and reduce the dimensionality of sensor image information, resulting in the optimal number of principal components (PCs). Finally, three different models based on the sensor image features were constructed for analysis. These models included the support vector machine (SVM), the random forest (RF) and the kernel based extreme learning machine (KELM). In process of model correction, the parameters of the three models were optimized by dung beetle optimizer (DBO) algorithm, resulting in the identification of the optimal parameters. The results show that the cumulative variance contribution increases slowly when the number of PCs starts from 6, and the three models (i.e. DBO-SVM, DBO-RF, and DBO-KELM) have the best prediction results when PCs = 6. Compared to the DBO-RF and DBO-KELM models, the training and prediction performance of the SVM model based on the DBO optimization algorithm is more stable and has the higher recognition accuracy of the samples, reaching 100%. The results show that it is feasible to use olfactory visualization technology to achieve high-precision qualitative identification of edible oil pesticide residues.

Emergency evacuation risk assessment method for educational buildings based on improved extreme learning machine

In educational facility interiors, the risk of congestion and trampling among occupants during the evacuation process presents a significant safety concern. Therefore, assessing the risk of the evacuation process is of great practical and academic importance. To meet the requirements of rapid and timely risk assessment, this article proposes an emergency evacuation risk assessment model based on the Improved Extreme Learning Machine (ELM). The ELM with fast learning speed and good generalization performance is improved to form the Deep Extreme Learning Machine (DELM) and Kernel Based Extreme Learning Machine (KELM) models, and the Improved Seagull Optimization Algorithm (ISOA) was used to constitute the ISOA-DELM and ISOA-KELM models for training. Taking a university library as an example, the evaluation process of model data acquisition, training, and testing is analyzed and compared. The prediction accuracy of the ISOA-DELM and ISOA-KELM models proposed in this paper reached more than 92%. The results show that improved extreme learning machine models can enable an efficient and fast risk assessment.

Prediction of Regional Carbon Price in China Based on Secondary Decomposition and Nonlinear Error Correction

Effective prediction of carbon prices matters a great deal for risk management in the carbon financial market. This article designs a blended approach incorporating secondary decomposition and nonlinear error-correction technology to predict the regional carbon price in China. Firstly, the variational mode decomposition (VMD) method is used to decompose the carbon price, and then, the time-varying filter-based empirical mode decomposition (TVFEMD) is introduced to decompose the residual term generated by VMD, and the multiple kernel-based extreme learning machine (MKELM) optimized by the sparrow search algorithm (SSA) is innovatively built to forecast the carbon subsequences. Finally, in order to mine the hidden information contained in the forecasted error, the nonlinear error-correction method based on the SSA-MKELM model is introduced to correct the initial prediction of carbon price. The empirical results show that the proposed model improves the prediction accuracy of carbon prices, with RMSE, MAE, MAPE, and DS up to 0.1363, 0.1160, 0.0015, and 0.9231 in Guangdong, respectively. In the case of the Hubei market, the model also performs best. This research innovatively expands the prediction theory and method of China’s regional carbon price.

Error-distribution-free kernel extreme learning machine for traffic flow forecasting

Traffic flow modeling plays a crucial role in intelligent transportation systems, which is of vital significance for mitigating traffic congestion and reducing carbon emissions. Owing to the uncertainties and nonlinear characteristics of traffic flow, it confronts a considerable challenge to establish a model to predict traffic flow efficiently and robustly. Kernel-based extreme learning machine (KELM), a natural extension of extreme learning machine (ELM) that incorporates kernel learning, has demonstrated excellent performance in traffic flow prediction. However, the performance of KELM may significantly decrease when the noise is non-Gaussian, as it was developed under the minimum mean square error (MMSE) criterion assuming Gaussian noise. To address this issue, we propose an error-distribution-free kernel extreme learning machine, termed ɛDFKELM, by embedding a more robust optimization criterion to guide the training. In addition, we further develop an online version of the ɛDFKELM model for continual forecasting, called ɛDFKELMv2. We perform extensive experiments on two widely-used public benchmark traffic flow datasets, which illustrate that the ɛDFKELM model outperforms the state-of-the-art approaches in terms of forecasting performance. ɛDFKELM model achieves RMESE values of 251.49 vehs/h, 196.27 vehs/h, 216.97 vehs/h, and 160.92 vehs/h on the A1, A2, A4, and A8 highways of Amsterdam dataset, respectively.

Characterization of Pepper Ripeness in the Field Using Hyperspectral Imaging (HSI) with Back Propagation (BP) Neural Network and Kernel Based Extreme Learning Machine (KELM) Models

Due to the economic differences of chili peppers at different maturity levels and the absence of differentiation of maturity in existing harvesting processes, it is crucial to accurately identify the maturity during harvesting for improving economic benefits. This paper investigated pepper maturity recognition models using hyperspectral technology to realize intelligent pepper harvesting with high accuracy and monitor maturity. Hyperspectral data (400–1000 nm) for field line peppers were collected, preprocessed using normalization, Savitzky-Golay convolutional smoothing, and standard normal variable transformation and subsequently used to train back propagation neural network (BP) and kernel based extreme learning machine (KELM) models. Principal component analysis (PCA) was used to reduce data dimensionality and identify characteristic spectral wavelengths for pepper maturity at 862.2, 676.9, 578.1, and 980.7 nm; and BP, PCA-BP, KELM, and PCA-KELM models were subsequently established. The precision for the KELM and PCA-KELM models (99.5% and 97.3%, respectively) was superior to the other two models; however, the PCA-KELM model used only four of the feature wavelengths as inputs, hence it required only 1/44 the data compared with the KELM model. Thus, the PCA-KELM model achieved high recognition accuracy and training speed, offering an effective method to discriminate pepper ripeness based on hyperspectral features.

A temporal–spatiotemporal domain transformation-based modeling method for nonlinear distributed parameter systems

Abstract Complex nonlinear distributed parameter systems (DPSs) exist widely in advanced industrial thermal processes. The modeling of such highly nonlinear systems is a challenge for traditional time/space-separation-based methods since they employ linear methods for the model reduction and spatiotemporal reconstruction, which may lead to an inefficient application of the nonlinear spatial structure features represented by the spatial basis functions. To overcome this problem, a novel spatiotemporal modeling framework composed of nonlinear temporal domain transformation and nonlinear spatiotemporal domain reconstruction is proposed in this paper. Firstly, local nonlinear dimension reduction based on the locally linear embedding technique is utilized to perform nonlinear temporal domain transformation of the spatiotemporal output of nonlinear DPSs. In this step, the original spatiotemporal data can be directly transformed into low-order time coefficients. Then, the extreme learning machine (ELM) method is utilized to establish a temporal model. Finally, through the spatiotemporal domain reconstruction based on the kernel-based ELM method, the prediction of the temporal dynamics obtained from the temporal model can be reconstructed back to the spatiotemporal output. The effectiveness and performance of the proposed method are demonstrated in experiments on the thermal processes of a snap curing oven and a lithium-ion battery.

The adaptive kernel-based extreme learning machine for state of charge estimation

The adaptive kernel-based extreme learning machine for state of charge estimation

Prediction of Incident Solar Radiation Using a Hybrid Kernel Based Extreme Learning Machine

Forecasting solar radiation for a given region is an emerging field of study. It will help to identify the places for installing large-scale photovoltaic-systems, designing energy-efficient buildings and energy estimation. The different machine learning kernel-based approaches for prediction problems uses either a local or global kernel. These models can provide either strong training capability or good generalization performance. In this paper, a new hybrid kernel is proposed using the combination of a local and global kernel. A novel algorithm Hybrid Kernel-based Extreme Learning Machine is proposed for predictive modelling of Incident Solar Radiation (ISR) time series using new hybrid kernel. The proposed algorithm uses the surface, atmospheric, cloud properties obtained from the MODIS instrument and observed ISR at time t – 1 to predict ISR at time t. This study is conducted for 41 diverse sites of Australia for the period of 2012–2015. Further, the proposed model is experimented with other time series datasets to prove its efficacy. It is shown that the proposed methodology outperforms five other benchmarking methods in terms of MAE and Willmott’s Index (WI). Therefore, the suggested approach can be used for modelling solar energy at a national scale using remotely-sensed satellite footprints.

A modified binary version of aphid–ant mutualism for feature selection: a COVID-19 case study

AbstractThe speedy development of intelligent technologies and gadgets has led to a drastic increment of dimensions within the datasets in recent years. Dimension reduction algorithms, such as feature selection methods, are crucial to resolving this obstacle. Currently, metaheuristic algorithms have been extensively used in feature selection tasks due to their acceptable computational cost and performance. In this article, a binary-modified version of aphid–ant mutualism (AAM) called binary aphid–ant mutualism (BAAM) is introduced to solve the feature selection problems. Like AAM, in BAAM, the intensification and diversification mechanisms are modeled via the intercommunication of aphids with other colonies’ members, including aphids and ants. However, unlike AAM, the number of colonies’ members can change in each iteration based on the attraction power of their leaders. Moreover, the second- and third-best individuals can take the place of the ringleader and lead the pioneer colony. Also, to maintain the population diversity, prevent premature convergence, and facilitate information sharing between individuals of colonies including aphids and ants, a random cross-over operator is utilized in BAAM. The proposed BAAM is compared with five other feature selection algorithms using several evaluation metrics. Twelve medical and nine non-medical benchmark datasets with different numbers of features, instances, and classes from the University of California, Irvine and Arizona State University repositories are considered for all the experiments. Moreover, a coronavirus disease (COVID-19) dataset is used to validate the effectiveness of the BAAM in real-world applications. Based on the acquired outcomes, the proposed BAAM outperformed other comparative methods in terms of classification accuracy using various classifiers, including K nearest neighbor, kernel-based extreme learning machine, and multi-class support vector machine, choosing the most informative features, the best and mean fitness values and convergence speed in most cases. As an instance, in the COVID-19 dataset, BAAM achieved 96.53% average accuracy and selected the most informative feature subset.

Identification and Prediction of Thermodynamic Disasters During Deep Coal Mining

Researching the methods to identify and predict thermodynamic disasters during deep coal mining is a very important work for the design of mine emergency systems and the decision-making of mine rescue and personnel evacuation, however, existing studies only built static models without evaluating or predicting the development trend of thermodynamic disasters, the research on dynamic modeling methods and rescue decision-making is insufficient, and they generally ignored the mechanism of mutual conversion between fire and gas explosion in deep coal mines. Thus, this paper aims to study the identification and prediction of thermodynamic disasters during deep coal mining. At first, the method for analyzing the thermal field in deep coal mining areas is introduced in detail, and the finite element thermal analysis method is adopted to study the thermodynamic disasters during deep coal mining; then, this paper establishes a thermodynamic disaster prediction model based on the improved Kernel-based Extreme Learning Machine (KELM), and introduces the improved Crow Search Algorithm (CSA) to solve the instability of prediction results caused by artificial selection of model parameters. At last, this paper uses experimental results to verify the validity of the proposed model.

Applicability of hybrid bionic optimization models with kernel-based extreme learning machine algorithm for predicting daily reference evapotranspiration: a case study in arid and semiarid regions, China.

The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources; this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO; the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.

Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine

Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonlinearity, nonstationarity, and multiple frequencies of regional carbon prices in China. This study constructs a model, called the VMD-ICEEMDAN-RE-SSA-HKELM model, to forecast regional carbon prices in China based on the idea of ‘decomposition–reconstruction–integration’. The VMD is first used to decompose carbon prices and the ICEEMDAN is then used to decompose the residual term that contains complex information. To reduce the systematic error caused by increases in the mode components of carbon price, range entropy (RE) is used to reconstruct the results of its secondary decomposition. Following this, HKELM is optimized by the sparrow search algorithm and used to forecast each subseries of carbon prices. Finally, predictions of the price of carbon are obtained by linearly superimposing the results of the forecasts of each of its subseries. The results of experiments show that the secondary decomposition strategy proposed in this paper is superior to the traditional decomposition strategy, and the proposed model for forecasting carbon prices has significant advantages over a considered reference group of models.

Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method

For short-term wind power prediction, a soft margin multiple kernel learning (MKL) method is proposed. In order to improve the predictive effect of the MKL method for wind power, a kernel slack variable is introduced into each base kernel to solve the objective function. Two kinds of soft margin MKL methods based on hinge loss function and square hinge loss function can be obtained when hinge loss functions and square hinge loss functions are selected. The improved methods demonstrate good robustness and avoid the disadvantage of the hard margin MKL method which only selects a few base kernels and discards other useful kernels when solving the objective function, thereby achieving an effective yet sparse solution for the MKL method. In order to verify the effectiveness of the proposed method, the soft margin MKL method was applied to the second wind farm of Tianfeng from Xinjiang for short-term wind power single-step prediction, and the single-step and multi-step predictions of short-term wind power was also carried out using measured data provided by alberta electric system operator (AESO). Compared with the support vector machine (SVM), extreme learning machine (ELM), kernel based extreme learning machine (KELM) methods as well as the SimpleMKL method under the same conditions, the experimental results demonstrate that the soft margin MKL method with different loss functions can efficiently achieve higher prediction accuracy and good generalization performance for short-term wind power prediction, which confirms the effectiveness of the method.

Modeling and prediction of TEC based on multivariate analysis and kernel-based extreme learning machine

Modeling and prediction of TEC based on multivariate analysis and kernel-based extreme learning machine

Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM

Accurate photovoltaic (PV) power prediction can guarantee the stable operation of a power system. However, complex environmental factors contribute to the chaotic nature of PV power sequences, consequently affecting the prediction accuracy. In this paper, an online PV power prediction model is proposed based on chaotic characteristic analysis, improved particle swarm optimization (PSO), and kernel-based extreme learning machine (KELM). The proposed method includes data pre-processing, offline parameters extraction and online prediction. At first, historical data are classified according to the degree of fluctuation of PV power and seasonal characteristics. The PV power sequences are filtered by singular spectrum analysis (SSA) to eliminate noise and outliers. Then, extracted parameters include the decomposing modes of variational mode decomposition (VMD) and the ideal parameters of phase space reconstruction (PSR) and KELM. The ideal parameters are searched by improved PSO. Finally, during the online prediction process, the training data needs to be reconstructed using VMD and PSR to update the network of KELM at each point prediction. Chaotic characteristic analysis is reflected in the application of SSA, VMD and PSR. Compared with the other seven different prediction methods, the experimental results verify that the proposed method is effective with higher accuracy.

Mixture Correntropy-Based Kernel Extreme Learning Machines.

Kernel-based extreme learning machine (KELM), as a natural extension of ELM to kernel learning, has achieved outstanding performance in addressing various regression and classification problems. Compared with the basic ELM, KELM has a better generalization ability owing to no needs of the number of hidden nodes given beforehand and random projection mechanism. Since KELM is derived under the minimum mean square error (MMSE) criterion for the Gaussian assumption of noise, its performance may deteriorate under the non-Gaussian cases, seriously. To improve the robustness of KELM, this article proposes a mixture correntropy-based KELM (MC-KELM), which adopts the recently proposed maximum mixture correntropy criterion as the optimization criterion, instead of using the MMSE criterion. In addition, an online sequential version of MC-KELM (MCOS-KELM) is developed to deal with the case that the data arrive sequentially (one-by-one or chunk-by-chunk). Experimental results on regression and classification data sets are reported to validate the performance superiorities of the new methods.