Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm

Through the accurate prediction of power load, the start and stop of generating units in the power grid can be arranged economically and reasonably. The safety and stability of power grid operation can be maintained. First, chicken swarm optimizer based on nonlinear dynamic convergence factor (NCSO) optimizer is proposed based on chicken swarm optimizer (CSO) optimizer. In NCSO optimizer, nonlinear dynamic inertia weight and levy mutation strategy are introduced. Compared with CSO optimizer, the convergence speed and effect of NCSO optimizer are obviously improved. Second, the random parameters of extreme learning machine (ELM) model are optimized by NCSO optimizer, and NCSOELM model is established to predict the power load. Finally, the NCSO optimization extreme learning machine (NCSOELM) model is used to predict the power load, and compared with back propagation (BP), support vector machine (SVM) and CSO optimization extreme learning machine (CSOELM) model. The experimental results show that the fitting accuracy of NCSOELM model is high, and the determination coefficient r 2 is above 90%. And the root mean square error value of the NCSOELM model is 0.87, 0.41, and 0.25 smaller than the root mean square error values of the support vector machine, BP, and CSOELM models, respectively. Experiments show that the model proposed in this study has high fitting effect and low prediction error, which is of positive significance for the realization of economic and safe operation of energy system.

Rapid detection of food quality indicators using ELM and near-infrared spectroscopy.

To solve the problems of low prediction accuracy, poor stability, and low calculation efficiency in runoff forecasting, this study develops an extreme learning machine (ELM) model based on improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The model first uses orthogonal triangular decomposition (QR) to redefine the ELM hidden layer output to construct the orthogonal triangular ELM (QR-ELM). It then introduces the online sequence mechanism (OS) into the QR-ELM model to construct online sequence OR-ELM (OS-QR-ELM), which can effectively improve the efficiency of the ELM models. Then, it uses the correlation wave extension method to extend both ends of the original signal to solve the end effect of CEEMDAN. Finally, it integrates CEEMDAN and OS-QR-ELM combined with parallel computing ideas to construct a parallel CEEMDAN-OS-QR-ELM runoff prediction method. The experimental results show that compared with the support vector regression method combined with empirical mode decomposition, the accuracy and reliability of the parallel CEEMDAN-OS-QR-ELM model was 1.09% and 8% higher, respectively. Compared with the ELM model, the efficiency of OS-QR-ELM was 5%.

Structural damage assessment is an important part of structural health monitoring. Nevertheless, there is the defect of low accuracy in the existed evaluation methods. In order to solve this problem, a new model for structural damage assessment using particle swarm optimization (PSO) and extreme learning machine (ELM) named PELM is proposed in this paper. Firstly, PSO is used to calculate the best parameters of ELM, and then the optimized ELM model is adopted to evaluate the structural damage. PELM model and other two different models are used to assess the structural damages of a pile structure as an experiment, the results show that PELM model has a higher prediction accuracy than SVM model and ELM model.

Predicting water consumption is of key importance for water supply management, which is also relevant in processes for reviewing prices.In this study, a hybrid method based on extreme learning machine model with the adaptive metrics of inputs is proposed for improving forecasting accuracy.The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and reduce the effect of the overfitting of networks.It was found that the proposed model is practical for water demand forecasting and outperforms the autoregression (AR), artificial neural network (ANN), support vector machine(SVM) and extreme learning machine (ELM) models.

A prediction method based on Extreme Learning Machines(ELM) is proposed. The parameters of ELM are optimized by immune optimization algorithm. The proposed model is used to predict the fiber quality of the melt spinning process. According to the limited samples, the spinning speed, the spinning temperature, the blowing speed and the blowing temperature of the melt spinning process are four key parameters that affect the fiber quality. The four indexes of polyester fiber are 1.5 times rate of elongation(EYS1.5), elongation inequality rate(EYSCV), breaking strength(DT) and elongation capacity(DE). We collect actual data samples in the factory and establish the ELM prediction model. The ELM model optimized by immune optimization algorithm (ELM-IA) is compared with PSO optimized ELM (ELM-PSO), ELM model and BP neural network. The experimental results show that the ELM-IA model is better than the ELM-PSO model, the ELM model and BP neural network in prediction. The ELM-IA model has a good generalization ability. It can play a guiding role in the actual fiber production process.

This study presents a comparative analysis of hybrid Extreme Learning Machine (ELM) models optimized with metaheuristic algorithms Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), and Grey Wolf Optimizer (GWO) for real-time Rate of Penetration (ROP) prediction in drilling operations. The study aims to improve ROP prediction accuracy and computational efficiency while addressing the challenges of real-time implementation in dynamic drilling environments. The methodology involves a formation-specific modelling approach, where separate ELM models are trained for each formation using surface drilling parameters such as weight on bit (WOB), rotary speed (RPM), and flow rate. Metaheuristic algorithms optimize the ELM’s weights and biases to improve predictive performance. The models were trained and validated using a dataset of 13,262 data points from an Algerian field, with different statistical metrics used for evaluation. Sensitivity analysis using SHapley Additive exPlanations (SHAP) identified drilling torque and standpipe pressure as key ROP influencers. Results indicate that all hybrid models outperform standalone ELM, with ELM-GWO achieving the highest accuracy and fastest convergence. The real-time modelling framework, incorporating incremental learning and formation-based model recalibration, which ensures robust and adaptive ROP predictions and minimizing reliance on difficult-to-measure parameters such as unconfined compressive strength (UCS). This work contributes to the existing body of literature by introducing a formation-specific and real-time hybrid ELM modelling approach, demonstrating its potential for ROP optimization and cost reduction in petroleum drilling.

Intrusion detection is an important issue for network security. Many machine learning based detection technologies have been put forward and obtained rather good results, such as Bayesian classifiers, Extreme learning machine. However, the performance of these methods is dependent on parameters to a great extent. Aiming at learning the optimal parameters of extreme learning machine for network intrusion detection, the paper proposes a new network intrusion detection based on hybrid rice algorithm optimized extreme learning machine (HRO-ELM). First, the extreme learning machine parameters are encoded as rice gene location, and the accuracy of test results represents the fitness value of the algorithm. Then the optimal parameters of the extreme learning machine are found by simulating rice breeding behavior, and a network intrusion detection classifier is established. Finally, the KDD99 data set is used and simulation results show that the HRO-ELM improves the accuracy of network intrusion detection and can meet the requirements of network intrusion online detection.

Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq

The dynamic thermomechanical properties of wood–plastic composites (WPCs) are influenced by various factors, such as the selection of raw materials and processing parameters. To investigate the effects of different wood fiber content ratios and temperature on the loss modulus of WPCs, seven different proportions of Masson pine (Pinus massoniana Lamb.) and Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] mixed-fiber-reinforced HDPE composites were prepared using the extrusion molding method. Their dynamic thermomechanical properties were tested and analyzed. The storage modulus of WPCs showed a decreasing trend with increasing temperature. A reduction in the mass ratio of Masson pine wood fibers to Chinese fir wood fibers resulted in an increase in the storage modulus of WPCs. The highest storage modulus was achieved when the mass ratio of Masson pine wood fibers to Chinese fir wood fibers was 1:5. In addition, the loss modulus of the composites increased as the content of Masson pine fiber decreased, with the lowest loss modulus observed in HDPE composites reinforced with Masson pine wood fibers. The loss tangent for all seven types of WPCs increased with rising temperatures, with the maximum loss tangent observed in WPCs reinforced with Masson pine wood fibers and HDPE. A prediction method based on the Extreme Learning Machine (ELM) model was introduced to predict the dynamic thermomechanical properties of WPCs. The prediction accuracy of the ELM model was compared comprehensively with that of other models, including Support Vector Machines (SVMs), Random Forest (RF), Back Propagation (BP) neural networks, and Particle Swarm Optimization-BP (PSO-BP) neural network models. Among these, the ELM model showed superior data fitting and prediction accuracy, with an R2 value of 0.992, Mean Absolute Error (MAE) of 1.363, and Root Mean Square Error (RMSE) of 3.311. Compared to the other models, the ELM model demonstrated the best performance. This study provides a solid basis and reference for future research on the dynamic thermomechanical properties of WPCs.

Air overpressure (AOp) is a hazardous effect induced by the blasting method in surface mines. Therefore, it needs to be predicted to reduce the potential risk of damage. The aim of this study is to offer an efficient method to predict AOp using a cascaded forward neural network (CFNN) trained by Levenberg–Marquardt (LM) algorithm, called the CFNN-LM model. Additionally, a generalized regression neural network (GRNN) and extreme learning machine (ELM) were employed to demonstrate the accuracy level of the proposed CFNN-LM model. To conduct the CFNN-LM, GRNN, and ELM models, an extensive database, related to four quarry sites in Malaysia, was used including 62 sets of dependent and independent parameters. Next, the performances of the aforementioned models were checked and discussed through statistical criteria and efficient graphical tools. Finally, the results showed the superiority of CFNN-LM (R2 = 0.9263 and RMSE = 3.0444) over GRNN (R2 = 0.7787 and RMSE = 5.1211) and ELM (R2 = 0.6984 and RMSE = 6.2537) models in terms of prediction accuracy. Furthermore, three different regression analysis metrics were used to perform the sensitivity analysis, and according to the obtained results, the maximum charge per delay ( $$\beta$$ = 0.475, SE = 0.115, t-test = 4.125) was considered as the most influential feature in modeling the AOp.

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

This chapter examines the capability of Minimax Probability Machine Regression (MPMR) and Extreme Learning Machine (ELM) for prediction of Optimum Moisture Content (OMC), Maximum Dry Density (MDD) and Soaked California Bearing Ratio (CBR) of soil. These algorithms can analyse data and recognize patterns and are proved to be very useful for problems pertaining to classification and regression analysis. These regression models are used for prediction of OMC and MDD using Liquid limit (LL) and Plastic limit (PL) as input parameters. Whereas Soaked CBR is predicted using Liquid limit, Plastic limit, OMC and MDD as input parameters. The predicted values obtained from the MPMR and ELM models have been compared with that obtained from Artificial Neural Networks (ANN). The accuracy of MPMR and ELM models, their performance and their reliability with respect to ANN models has also been evaluated.

ENIC: Ensemble and Nature Inclined Classification with Sparse Depiction based Deep and Transfer Learning for Biosignal Classification

This paper presents a reliable on-line re-optimization control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine. In order to overcome the difficulty in developing detailed mechanistic models, extreme learning machine (ELM) based data driven models are developed. In building an ELM model, the hidden layer weights are randomly assigned and the output layer weights are obtained in a one step regression type of learning. This feature makes the development of ELM very fast. A single ELM model can lack of robustness due the randomly assigned hidden layer weights. To overcome this problem, multiple ELM models are developed from bootstrap re-sampling replications of the original training data and are then combined. In addition to enhanced model accuracy, bootstrap aggregated ELM can also give model prediction confidence bounds. A reliable optimal control policy is achieved by means of the inclusion of model prediction confidence bounds within the optimization objective function to penalize wide model prediction confidence bounds which are associated with uncertain predictions as a consequence of plant model-mismatch. Finally, in order to deal with unknown process disturbances, an on-line re-optimization control strategy is developed in that on-line optimization is carried out while the batch process is progression. The proposed technique is successfully implemented on a simulated fed-batch fermentation process.