Multi-kernel Extreme Learning Machine Research Articles

Short-Term Electrical Load Forecasting Based on Fuzzy Rough Set Feature Selection and Multi-kernel Extreme Learning Machine

As the complexity of power systems increases, accurate load forecasting becomes crucial. This paper proposes a method for short-term electrical load forecasting that integrates fuzzy rough set (FRS) theory and multi-kernel extreme learning machine (MKELM) to improve both the accuracy and reliability of load predictions. First, we introduce the FRS theory for pre-selecting features. Next, we use correlation analysis (CA) to get rid of redundant features and choose the most important ones as prediction targets. Second, we introduce a novel prediction model based on the multi-kernel extreme learning machine (MKELM), utilizing an enhanced differential evolution algorithm (DEA) to optimize the kernel function’s parameters and the model’s weights. This approach allows for effective adaptation to various feature subsets. Experimental results on actual power load data demonstrate that our approach achieves high accuracy and reliability in short-term load forecasting. Moreover, comparative evaluations reveal that the proposed method outperforms alternative prediction models on key metrics. ANOVA and multiple comparisons further validate the statistical significance and superiority of the proposed method.

A single-joint multi-task motor imagery EEG signal recognition method based on Empirical Wavelet and Multi‐Kernel Extreme Learning Machine

BackgroundIn the pursuit of finer Brain-Computer Interface commands, research focus has shifted towards classifying EEG signals for multiple tasks. While single-joint multitasking motor imagery provides support, distinguishing between EEG signals from the same joint remains challenging due to their similar brain spatial distribution. New methodWe designed experiments involving three motor imagery tasks—wrist extension, wrist flexion, and wrist abduction—with six participants. Based on this, a single-joint multi-task motor imagery EEG signal recognition method using Empirical Wavelet Decomposition and Multi-Kernel Extreme Learning Machine is proposed. This method employs Empirical Wavelet Decomposition (EWT) for modal decomposition, screening, and reconstruction of raw EEG signals, feature extraction using Common Spatial Patterns (CSP), and classification using Multi-Kernel Extreme Learning Machine (MKELM). ResultsAfter EWT processing, differences in time and frequency characteristics between EEG signals of different classes were enhanced, with the MKELM model achieving an average recognition accuracy of 91.93 %. Comparison with other methods and conclusionsWe compared EWT with Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), Local Mean Decomposition (LMD), and Wavelet Packet Decomposition (WPD). The results showed that the differences between various types of EEG signals processed by EWT were the most pronounced. The MKELM model outperformed traditional machine learning models such as Extreme Learning Machine (ELM), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) in terms of recognition performance, and also exhibited faster training speeds than deep learning models such as Bayesian Convolutional Neural Network (BCNN) and Attention-based Dual-scale Fusion Convolutional Neural Network (ADFCNN). In summary, the proposed method provides a new approach for achieving finer Brain-Computer Interface commands.

Optimization of short-term wind power prediction of Multi-kernel Extreme Learning Machine based on Sparrow Search Algorithm

Aiming at the problem that the single kernel function of kernel extreme learning machine (KELM) cannot adapt to the variable actual wind power. This paper proposes a modified prediction model which can increase the accuracy of prediction. The prediction model uses multiple kernel functions instead of a single kernel function and optimizes the kernel parameters by using a sparrow search algorithm (SSA). Finally, through the simulation and comparison experiments, the proposed prediction model has better prediction accuracy than the conventional prediction model.

Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future.

Stock Market prediction using hybrid multi-layer decomposition and optimized multi-kernel extreme learning machine

Stock Market prediction using hybrid multi-layer decomposition and optimized multi-kernel extreme learning machine

Swarm intelligence machine-learning-assisted progressive global optimization of DNAPL-contaminated aquifer remediation strategy

Abstract Remediation projects of DNAPL-contaminated groundwater generally face difficulties of low contaminant removal rate and high remediation cost. Hence, a machine-learning-assisted mixed-integer multi-objective optimization technique was presented for efficiently programming remediation strategies. A swarm intelligence multi-kernel extreme learning machine (SI-MKELM) was proposed to build a reliable intelligent surrogate model of the multiphase flow numerical simulation model for reducing the computational cost of repetitive CPU-demanding remediation efficiency evaluations, and a hyper-heuristic homotopy algorithm was developed for progressively searching the global optimum of the remediation strategy. The results showed that: (1) The multi-kernel extreme learning machine improved by swarm intelligence algorithm significantly improved the approximation accuracy to the numerical model, and the mean residual and mean relative error were only 0.7596% and 1.0185%, respectively. (2) It only took 0.1 s to run the SI-MKELM. Replacing the numerical model with SI-MKELM considerably reduced the computational burden of the simulation–optimization process and maintained high computational accuracy for optimizing the DNAPL-contaminated aquifer remediation strategy. (3) The hyper-heuristic homotopy algorithm was capable of progressively searching the global optimum, and avoiding premature convergence in the optimization process. It effectively improved the searching ability of the traditional heuristic algorithms.

Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine

Multi-step wind speed prediction based on an improved multi-objective seagull optimization algorithm and a multi-kernel extreme learning machine

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intense noise. Therefore, it is still a great challenge to make early fault diagnosis. An intelligent early fault diagnosis method for rotating machinery is proposed based on the parameter optimization of the variational mode decomposition (VMD) and deep multi-kernel extreme learning machine (DMKELM). Firstly, the improved whale optimization algorithm (IWOA) is designed by introducing the iterative chaotic mapping, nonlinear convergence factor and inertia weight to optimize the VMD parameters. Secondly, the optimized VMD (OVMD) with sample entropy is created to reduce noise and reconstruct the signals. Finally, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function, which can enhance the classification performance and generalization ability of the model. Two experiments on bearings and gears show that the fault diagnosis accuracy by DMKELM is 99 and 98.5%, respectively, which is at least 1% higher than comparative methods and increases by 4% after noise reduction. The result shows that the proposed method has great superiority in the early fault diagnosis of rotating machinery.

High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

High-impedance fault identification and location by using mode decomposition integrated adaptive multi-kernel extreme learning machine technique for distributed generator-based microgrid

Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification.

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.

Life Prediction of the Gear Transmission System with Multicharacteristics

This study obtains and predicts multifault data in the key transmission and connection systems with gears. Model building is based on the multikernel extreme learning machine with the method of maximum correlation kurtosis deconvolution and variational mode decomposition. To this end, the realization form of the life prediction is first studied by enhancing the low-frequency signal. Then, the larger correlation coefficient is selected as the sensitive feature parameter aiming at mapping to a feature space by the randomly initialized hidden layer in the learning machine, and the weight value of output layer is obtained using the least square method. A case study on the fault diagnosis of gear transmission system is conducted in the end to illustrate the proposed approach.

Detection and classification of islanding by using variational mode decomposition and adaptive multi-kernel based extreme learning machine technique

In this work, a six bus IEC standard model is simulated under MATLAB/SIMULINK environment with different penetration levels of islanding i.e., small, medium, and large scenarios with radial as well as in looped configuration. The three-phase current signals are extracted from each end of buses and synthesized through variational mode decomposition (VMD) to compute the spectral energy. Thereafter, subsequent features are obtained and fed into a novel adaptive multi-kernel extreme learning machine (AMKELM) classifier to classify the islanding and non-islanding cases. Different switching conditions, low impedance, and high impedance faults, and interference of noise are purposefully taken into account by looking at the real-time conditions. Finally, the performance of the proposed technique is compared with other recently published research works as well as with traditional ones to prove its efficacy over other techniques for the detection and classification of islanding scenarios.

Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble

ABSTRACT Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ensemble hybrid forecasting approach. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to effectively decompose the original time series into multiple relatively easy subseries, which reduces the complexity of the data. Secondly, sample entropy calculates the complexity of a sequence, and then adopts the elbow rule to adaptively divide them into different complex sets. Finally, multi-kernel extreme learning machine (KELM) models are used to forecast the components of different sets and integrate them. This hybrid approach makes full use of the advantages of different models, which enables effective use of data. The empirical results demonstrate that the approach can both produce results that are close to the actual values and be utilized as a strategy for forecasting daily tourism volume.

Multi‐objective auto‐encoder deep learning‐based stack switching scheme for improved battery life using error prediction of wind‐battery storage microgrid

For any wind power generation system, battery energy storage is a suitable backup power unit for ensuring greater functionality by compensating the prediction error due to the variable nature of generation. For designing local energy management system, the hierarchical operation of distributed generation controllers, BES life, etc. is directly influenced by the prediction error profile. To design an effective local energy management system, it is imperative to obtain wind power generator control reference influencing the overall system stability and reducing the deterioration of life and power loss of the integrated battery energy storage system due to increased temperature. Thus, an efficient local energy management system for the doubly fed induction generator battery energy storage system is proposed here for the robust minimization of the prediction error and to increase controller responses and the life of the battery. A novel deep learning robust multilayer multi-kernel extreme learning machine autoencoder algorithm is proposed here in order to obtain an improvement in the prediction error profile. This prediction algorithm produces optimal model generalization along with the minimization of reconstruction errors and uses simple matrix inversion for prediction in comparison with more complex deep learning neural networks. A new secondary controller is proposed to address the degradation of the life of the battery energy storage system due to increased temperature/power loss profile under prediction error. A model reference-based battery temperature model, associated with multi-objective optimization-based temperature tolerance dynamic stack reconfiguration of battery stacks is incorporated in the proposed secondary controller. This secondary controller-based local energy management system operation is targeted towards satisfying local load demand and grid dispatch, while ensuring an optimized battery stack performance under wind power generation discrepancies. In addition, a new adaptive slope-based primary controller is designed for effective power sharing between the battery energy storage system and wind power generator based on % state of the charge of the energy storage system. Also, an independent distributed generator controller is studied in an elaborate manner for the DC link stability of the DC-DC converter and voltage source converter for grid synchronization control.

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

Deterministic multikernel extreme learning machine with fuzzy feature extraction for pattern classification

Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.

Hybrid homotopy-PSO global searching approach with multi-kernel extreme learning machine for efficient source identification of DNAPL-polluted aquifer

Groundwater pollution source identification (GPSI), which is critical for taking effective measures to protect groundwater resources, assess risks, and design remediation strategies, typically involves the solution of a nonlinear and ill-posed inverse problem. Regarding the inversion of dense non-aqueous phase liquid (DNAPL) sources, the special characteristics of pollutants render related research more complex. In the present study, homotopy-based optimization inverse theory and multi-kernel extreme learning machine (MK-ELM) were combined for efficiently solving GPSI problem while estimating aquifer parameters at a DNAPL-polluted site. The extreme learning machine incorporating multi kernels and whose parameters are obtained by means of a genetic algorithm (GA) was embedded in an optimization model for GPSI to replace the multiphase flow simulation model and to mitigate the considerable computational burdens of inversion iteration. The hybrid homotopy-particle swarm optimization (PSO) algorithm was constructed as a more efficient method for segmentally searching the global optimum in wide areas with low dependence on initial values. Results showed that the application of GA-based MK-ELM and hybrid homotopy-PSO effectively accomplish the simultaneous identification of source characteristics and aquifer parameters. The MK-ELM approximate the outputs of multiphase flow simulation model sufficiently with the certainty coefficient (R2) increased to 0.9982, whereas the mean relative error was limited to 1.5168%. Compared to the widely used PSO algorithm, the hybrid homotopy-PSO algorithm significantly reduced the mean relative error of identification results from 6.77% to 2.89%.

Prediction of landslide displacement using multi-kernel extreme learning machine and maximum information coefficient based on variational mode decomposition: a case study in Shaanxi, China

Prediction of landslide movement is an efficient approach in the reduction in landslide risk. However, it is also a tough task due to the scientific challenges in forecasting a sophisticated natural disaster. This paper proposes a VMD-MIC-M-KELM (variational mode decomposition-maximum information coefficient-multi-kernel extreme learning machine) technique for prediction of landslide movements. The original displacement is first decomposed into a predefined number of components by VMD. Then, the triggers of each component are selected based on MIC between subseries and influencing factors. The decomposed terms are predicted by M-KELM respectively via k-fold cross-validation. Finally, predicted total displacement is achieved by summing up all forecasting subseries. A case study of Miaodian landslide (China) is presented for validation of the developed model. The verification results demonstrate the higher ability of the approach to forecast monthly displacement for periods up to 12 months as compared to the Poly-KELM and SVR models. Thus, improved monthly predictions may be achieved with constantly updated datasets from the monitoring system, which would offer reliable information for early warning of landslide.

Maximum power point tracking algorithm of PV system based on irradiance estimation and multi-Kernel extreme learning machine

This paper proposes a maximum power point tracking (MPPT) algorithm based on irradiance estimation and multi-kernel extreme learning machine (MKELM) to reduce investment costs and improve PV system efficiency. First, because irradiance sensors are relatively expensive, an irradiance estimation method based on the grey wolf optimization (GWO) algorithm was used to replace the sensors to estimate irradiance value. Next, a prediction model based on MKELM was used to model the PV system. By inputting temperature and irradiance, the prediction model can output a reference voltage of the maximum power point (MPP), allowing the system to operate at the MPP. Experimental results showed that the irradiance estimation method based on GWO can accurately estimate irradiance value in real time, and the MKELM-based prediction model is highly accurate. Through simulation experiments on the PV system, validity and advantages of the proposed method over the traditional MPPT algorithm are verified under different operating environments.

An ELM-Based Semi-Supervised Indoor Localization Technique With Clustering Analysis and Feature Extraction

In this article, a new indoor localization technique with extreme learning machine (ELM) is proposed where only a small number of received signal strength indicator (RSSI) measurements are labeled. In the off-line learning phase, the iterative self organizing data analysis techniques algorithm (ISODATA) is used to divide the RSSI measurements into some measurement data subsets. Then the multi-kernel ELM (MK-ELM) method is utilized to perform classification learning and obtain the RSSI measurement classification function. For each RSSI measurement subset, a two-stage feature extraction algorithm using the kernel principal component analysis, the deep learning network and ELM method is proposed for RSSI measurement feature extraction. At last, the position regression function of each subset is obtained by the semi-supervised regression learning. In the on-line position estimation phase, using the measurement classification and feature extraction of the received RSSI measurement, the position is estimated based on the corresponding position regression function. The field tests show that the proposed algorithm can obtain more accurate position estimation than other existing ELM based localization approaches do.