Kernel Extreme Learning Machine Algorithm Research Articles

An efficient state-of-health estimation method for lithium-ion batteries based on feature-importance ranking strategy and hybrid kernel extreme learning machine algorithm

The safe and reliable operation of battery systems depends critically on accurately and dependably estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, accurate prediction of SOH still needs improvement due to the complex battery aging mechanism. This paper introduces a hybrid kernel extremum learning machine (HKELM) for estimating battery SOH. To improve estimation accuracy, we enhance the standard dung beetle optimization algorithm (DBO) with a new initialized population, adaptive weights method, and improved population diversification. We then use the enhanced IDBO algorithm to optimize the parameters of HKELM. This study examines two distinct anode types of LIBs from NASA and Oxford datasets. Fifty significant features are extracted from charging voltage, current, temperature, and incremental capacity (IC) curves. The importance indices of these features are then computed using statistical analyses, including Pearson's correlation coefficient and Spearman's rank correlation coefficient, followed by optimal ranking. The optimal number of final features is determined by comparing various combinations of high-level features, thus reducing model complexity and improving estimation accuracy. This paper proposes the IDBO-HKELM algorithm for SOH estimation. Compared to other methods, the algorithm shows superior estimation performance and anti-interference capability. Both B0007 and Cell8 estimate SOH with MAE < 0.15, RMSE <0.2, and R2 > 99.9 %. Experimental results demonstrate the robustness of the proposed method, achieving accurate and noise-immune SOH estimation.

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.

A novel adaptive weighted kernel extreme learning machine algorithm and its application in wind turbine blade icing fault detection

The conventional weighted kernel extreme learning machine (WKELM) is a typical cost-sensitive learning algorithm, which can provide a solution for the data imbalance problem in wind turbine blade icing fault detection. However, WKELM algorithm uses a fixed weighting strategy that only considers the number of samples to achieve imbalanced learning, which leads to its insufficient performance in data imbalance classification and fault detection. In this study, the support vector data description (SVDD) method is used to obtain sample distribution information to construct a novel adaptive weighting strategy. The adaptive weighting strategy is combined with the conventional fixed weighting strategy to construct an adaptive weighted kernel extreme learning machine (AWKELM) algorithm. The effectiveness and superiority of the AWKELM algorithm is verified through three benchmark cases, it is demonstrated that AWKELM algorithm can effectively solve the problem of data imbalance and further improve the effect of wind turbine blade icing detection.

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

Aiming at the difficulty in real-time measuring and the long offline measurement cycle for the content of cement clinker free lime (fCaO), it is very important to build an online prediction model for fCaO content. In this work, on the basis of Cholesky factorization, the online sequential multiple kernel extreme learning machine algorithm (COS-MKELM) is proposed. The LDLT form Cholesky factorization of the matrix is introduced to avoid the large operation amount of inverse matrix calculation. In addition, the stored initial information is utilized to realize online model identification. Then, three regression datasets are used to test the performance of the COS-MKELM algorithm. Finally, an online prediction model for fCaO content is built based on COS-MKELM. Experimental results demonstrate that the fCaO content model improves the performance in terms of learning efficiency, regression accuracy, and generalization ability. In addition, the online prediction model can be corrected in real-time when the production conditions of cement clinker change.

DCS-ELM: a novel method for extreme learning machine for regression problems and a new approach for the SFRSCC.

Extreme learning machine (ELM) algorithm is widely used in regression and classification problems due to its advantages such as speed and high-performance rate. Different artificial intelligence-based optimization methods and chaotic systems have been proposed for the development of the ELM. However, a generalized solution method and success rate at the desired level could not be obtained. In this study, a new method is proposed as a result of developing the ELM algorithm used in regression problems with discrete-time chaotic systems. ELM algorithm has been improved by testing five different chaotic maps (Chebyshev, iterative, logistic, piecewise, tent) from chaotic systems. The proposed discrete-time chaotic systems based ELM (DCS-ELM) algorithm has been tested in steel fiber reinforced self-compacting concrete data sets and public four different datasets, and a result of its performance compared with the basic ELM algorithm, linear regression, support vector regression, kernel ELM algorithm and weighted ELM algorithm. It has been observed that it gives a better performance than other algorithms.

Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on SSAE-KELM

Anaerobic digestion technology is the most environmentally friendly approach to treat kitchen waste. Volatile fatty acid (VFA) is an essential quality monitoring indicator in the anaerobic digestion process of treating kitchen waste. In this paper, a soft measurement method of volatile fatty acid (VFA) concentration in the anaerobic digestion process is established based on stacked supervised auto-encoder combine kernel extreme learning machine algorithm (SSAE-KELM) to improve the real-time monitoring level and resource conversion efficiency of the anaerobic digestion process. Given the problems of poor feature extraction and low accuracy and efficiency of the model, a stack supervised autoencoder is proposed to realize nonlinear and deep feature extraction of process data. Simultaneously, using the idea of the extreme learning machine to train the network significantly improves the efficiency of the model. Then, the kernel extreme learning machine is used to realize regression modelling. Besides, a combined feature selection algorithm is presented to select auxiliary variables more accurately. The simulation results demonstrate that the soft sensor model can predict the concentration of volatile fatty acids (VFA) more efficiently and accurately.

Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

Optimizing Extreme Learning Machines Using Chains of Salps for Efficient Android Ransomware Detection

Nowadays, smartphones are an essential part of people’s lives and a sign of a contemporary world. Even that smartphones bring numerous facilities, but they form a wide gate into personal and financial information. In recent years, a substantial increasing rate of malicious efforts to attack smartphone vulnerabilities has been noticed. A serious common threat is the ransomware attack, which locks the system or users’ data and demands a ransom for the purpose of decrypting or unlocking them. In this article, a framework based on metaheuristic and machine learning is proposed for the detection of Android ransomware. Raw sequences of the applications API calls and permissions were extracted to capture the ransomware pattern of behaviors and build the detection framework. Then, a hybrid of the Salp Swarm Algorithm (SSA) and Kernel Extreme Learning Machine (KELM) is modeled, where the SSA is used to search for the best subset of features and optimize the KELM hyperparameters. Meanwhile, the KELM algorithm is utilized for the identification and classification of the apps into benign or ransomware. The performance of the proposed (SSA-KELM) exhibits noteworthy advantages based on several evaluation measures, including accuracy, recall, true negative rate, precision, g-mean, and area under the curve of a value of 98%, and a ratio of 2% of false positive rate. In addition, it has a competitive convergence ability. Hence, the proposed SSA-KELM algorithm represents a promising approach for efficient ransomware detection.

An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine

Over the past years, the surge in the necessity for early detection and diagnosis of breast cancer has resulted in many innovative research directions. According to the World Health Organization, an early and accurate detection of breast cancer successfully leads to a correct decision towards its treatment. Development of computer-aided diagnosis (CAD) system is considered to be a major stead in current research practice to abet medical practitioners in decision-making. This paper proposes an improved CAD framework to correctly classify the digital mammograms into normal or abnormal, and further, benign or malignant. The proposed scheme employs a block-based discrete wavelet packet transform (BDWPT) to extract the features, namely, the Shannon entropy, Tsallis entropy, Renyi entropy, and energy. Then, principal component analysis (PCA) technique is utilized to extract the discriminating features from the original feature vector. Subsequently, an optimized wrapper-based kernel extreme learning machine (KELM) using a weighted chaotic salp swarm algorithm (WC-SSA) is proposed as classifier to classify the digital mammograms. Since the efficacy of KELM algorithm depends on its two important parameters, namely, the penalty parameter and the kernel parameter, the prime objective of the proposed work is to obtain the optimized value of the aforementioned parameters as well as to select the most relevant features from the reduced feature vector simultaneously.The proposed scheme is evaluated on three publicly available standard datasets, namely, MIAS, DDSM, and BCDR to validate the efficacy of the proposed BDWPT+PCA+WC-SSA-KELM scheme. The performance of the proposed model is evaluated in terms of different metrics, namely, classification accuracy, sensitivity, specificity, area under curve (AUC), Matthew’s correlation coefficient (MCC), and F-measure via a 5 × 5 stratified cross-validation approach. From the experimental results and their analysis, it is observed that for the normal–abnormal category, the proposed technique results in an accuracy of 99.62% and 99.92% for MIAS and DDSM, respectively, whereas in the case of benign–malignant classification, the proposed method yields an accuracy of 99.28%, 99.63%, 99.60% for MIAS, DDSM, and BCDR datasets, respectively. Further, it is also observed that the proposed WC-SSA-KELM scheme exhibits superior performance as compared to that of its counterparts. Additionally, two well-known statistical analyses, namely, ANOVA and Friedman tests are performed to demonstrate that the performance of the proposed scheme is significantly better than that of the other existing schemes.

Kernel extreme learning machine with iterative picking scheme for failure diagnosis of a turbofan engine

The kernel extreme learning machine (KELM) has attracted attention for failure diagnosis of turbofan engines, but its application for time-sensitive scenarios is inherently limited by its lack of sparseness. The original KELM constructs the hidden layer using all the training samples; thus, the real-time performance may be seriously degraded for large datasets. To address this limitation, a novel iterative picking scheme for improving the sparseness of KELM is proposed in this study. It has two noteworthy features, a compact structure and a sparse solution, which gives better real-time performance. The proposed scheme improves the sparseness of the KELM algorithm by two alternating components, an insertion strategy to expand the network and an elimination strategy to reduce the scale of the network. Validation on regression and classification benchmark datasets demonstrates that the proposed algorithm can produce a sparse network structure with fewer hidden nodes without sacrificing the model's accuracy. Finally, simulations of failure diagnosis of a turbofan engine show that the proposed algorithm can perform at an accuracy comparable to that of KELM with a much faster testing speed, which is crucial in real-time and onboard applications.

Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

Data-Driven Multiobjective Optimization for Burden Surface in Blast Furnace With Feedback Compensation

In this paper, an intelligent data-driven optimization scheme is proposed for finding the proper burden surface distribution, which exerts large influences on keeping blast furnace running smoothly in an energy-efficient state. In the proposed scheme, production indicators prediction models are first developed using a kernel extreme learning machine algorithm. To heel, burden surface decision is presented as a multiobjective optimization problem for the first time and solved by a modified two-stage intelligent optimization strategy to generate the initial setting values of burden surface. Furthermore, considering the existence of the approximation error of the created prediction models, feedback compensation is implemented to enhance the reliability of the results, in which an improved association rule mining method is developed to find the corrected values to compensate the initial setting values. Finally, we apply the proposed optimization scheme to determine the setting values of burden surface using actual data, and experimental results illustrate its effectiveness and feasibility.

An Online Calibration Method for a Galvanometric System Based on Wavelet Kernel ELM.

The online calibration method of a two-dimensional (2D) galvanometer requires both high precision and better real-time performance to meet the needs of moving target position measurement, which presents some challenges for traditional calibration methods. In this paper, a new online calibration method is proposed using the wavelet kernel extreme learning machine (KELM). Firstly, a system structure is created and its experiment setup is established. The online calibration method is then analyzed based on a wavelet KELM algorithm. Finally, the acquisition methods of the training data are set, two groups of testing data sets are presented, and the verification method is described. The calibration effects of the existing methods and wavelet KELM methods are compared in terms of both accuracy and speed. The results show that, for the two testing data sets, the root mean square errors (RMSE) of the Mexican Hat wavelet KELM are reduced by 16.4% and 38.6%, respectively, which are smaller than that of the original ELM, and the standard deviations (Sd) are reduced by 19.2% and 36.6%, respectively, indicating the proposed method has better generalization and noise suppression performance for the nonlinear samples of the 2D galvanometer. Although the online operation time of KELM is longer than ELM, due to the complexity of the wavelet kernel, it still has better real-time performance.

A Fast Fault Diagnosis Method of the PEMFC System Based on Extreme Learning Machine and Dempster–Shafer Evidence Theory

For purpose of solving the data-driven failure diagnosis problems of the proton exchange membrane fuel cell (PEMFC) system, improve the test accuracy and shorten the training time, a novel failure diagnosis method of the PEMFC systems based on data fusion is proposed, which combines extreme learning machine (ELM) and Dempster-Shafer (D-S) evidence theory. The characteristic vector extraction is carried out on the electrical quantities and the nonelectrical quantities of the PEMFC system under four different faults. The kernel ELM algorithm and online sequential ELM algorithm are, respectively, used to establish the failure diagnosis model of the PEMFC system based on electrical quantities and nonelectrical quantities. It is used for preliminary failure diagnosis of a PEMFC system. The diagnosis results of the above-mentioned two strategies are converted into the function values of the basic probability assignment by the squeeze function. The D-S evidence theory algorithm is used to fuse the diagnostic output at the decision level. The classification results of 154 samples of PEMFC system show that the novel model can diagnose four different degrees of high air stoichiometry failures. The average recognition rate is 98.70% and the operation time is only 0.2011 s. At the same time, the comparisons with the back-propagation neural network and one-against-one support vector machine show that the data fusion algorithm can significantly improve the running speed while ensuring the correct recognition rate. It can be used for online failure diagnosis of the PEMFC systems.

A new technique for ECG signal classification genetic algorithm Wavelet Kernel extreme learning machine

The examination and classification of Electrocardiogram (ECG) records have become particularly significant for diagnosing heart diseases. Machine learning methods are widely used in classifying ECG signals. In this study, Physikalisch-Technische Bundesanstalt Diagnostic ECG Database (PTBDB) from Physionet Database was used to classify ECG signals. Pan-Tompkins algorithm and Discrete Wavelet Transform (DWT) methods were used for extracting critical points such as QRS complex, PR, ST and QT of ECG signal. Afterwards, Traditional Extreme Learning Machine (ELM) was implemented to the ECG signal. Finally, Genetic Algorithm on software Genetic Algorithm Wavelet Kernel Extreme Learning Machine was improved for the determination of the coefficients, which were used in the Wavelet Kernel Extreme Learning Machine algorithm, in which wavelet function was accomplished. In this scope, it was observed that best classification performance values were reached to Acc 95%, Se 100% and Spe 80% with the implementation developed with the Genetic algorithm.

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine.

The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

Approximate kernel extreme learning machine for large scale data classification

In this paper, we propose an approximation scheme of the Kernel Extreme Learning Machine algorithm for Single-hidden Layer Feedforward Neural network training that can be used for large scale classification problems. The Approximate Kernel Extreme Learning Machine is able to scale well in both computational cost and memory, while achieving good generalization performance. Regularized versions and extensions in order to exploit the total and within-class variance of the training data in the feature space are also proposed. Extensive experimental evaluation in medium-scale and large-scale classification problems denotes that the proposed approach is able to operate extremely fast in both the training and test phases and to provide satisfactory performance, outperforming relating classification schemes.

A kernel extreme learning machine algorithm based on improved particle swam optimization

Kernel extreme learning machine (KELM) increases the robustness of extreme learning machine (ELM) by turning linearly non-separable data in a low dimensional space into a linearly separable one. However, the internal power parameters of ELM are initialized at random, causing the algorithm to be unstable. In this paper, we use the active operators particle swam optimization algorithm (APSO) to obtain an optimal set of initial parameters for KELM, thus creating an optimal KELM classifier named as APSO-KELM. Experiments on standard genetic datasets show that APSO-KELM has higher classification accuracy when being compared to the existing ELM, KELM, and these algorithms combining PSO/APSO with ELM/KELM, such as PSO-KELM, APSO-ELM, PSO-ELM, etc. Moreover, APSO-KELM has good stability and convergence, and is shown to be a reliable and effective classification algorithm.

An efficient classification based on genetically optimised hybrid PCA-Kernel ELM learning

A new classifier based on genetically optimised kernel extreme learning machine (KELM) is presented here. Firstly, principal component analysis (PCA) is used to retrieve the important features from the datasets and further these features are used for classification. Classification is done by using the genetically optimised KELM algorithm in which the kernel parameters of the kernel function of ELM are optimised by using the genetic algorithm (GA). The present approach is investigated on eight benchmark biomedical datasets from UCI machine learning repository and AT&T face database to show its efficiency and effectiveness. The results are validated by using classification accuracy, ROC and cross validation. The results show that the proposed learning algorithm is better in terms of generalisation performance and learning speed compared to other state of the art learning algorithms.