Online Sequential Extreme Learning Machine Research Articles

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

Performance Comparison of Different HTM-Spatial Pooler Algorithms Based on Information-Theoretic Measures

Hierarchical temporal memory (HTM) is a promising unsupervised machine-learning algorithm that models key principles of neocortical computation. One of the main components of HTM is the spatial pooler (SP), which encodes binary input streams into sparse distributed representations (SDRs). In this paper, we propose an information-theoretic framework for the performance comparison of HTM-spatial pooler (SP) algorithms, specifically, for quantifying the similarities and differences between sparse distributed representations in SP algorithms. We evaluate SP's standalone performance, as well as HTM's overall performance. Our comparison of various SP algorithms using Renyi mutual information, Renyi divergence, and Henze–Penrose divergence measures reveals that the SP algorithm with learning and a logarithmic boosting function yields the most effective and useful data representation. Moreover, the most effective SP algorithm leads to superior HTM results. In addition, we utilize our proposed framework to compare HTM with other state-of-the-art sequential learning algorithms. We illustrate that HTM exhibits superior adaptability to pattern changes over time than long short term memory (LSTM), gated recurrent unit (GRU) and online sequential extreme learning machine (OS-ELM) algorithms. This superiority is evident from the lower Renyi divergence of HTM (0.23) compared to LSTM6000 (0.33), LSTM3000 (0.38), GRU (0.41), and OS-ELM (0.49). HTM also achieved the highest Renyi mutual information value of 0.79, outperforming LSTM6000 (0.73), LSTM3000 (0.71), GRU (0.68), and OS-ELM (0.62). These findings not only confirm the numerous advantages of HTM over other sequential learning algorithm, but also demonstrate the effectiveness of our proposed information-theoretic approach as a powerful framework for comparing and evaluating various learning algorithms.

AF-OS-ELM-MVE: A new online sequential extreme learning machine of dam safety monitoring model for structure deformation estimation

Dam deformation monitoring is an important task in hydraulic engineering projects. Traditional statistical methods and machine learning algorithms have been widely applied in structural safety prediction due to the application’s convenience. However, most machine learning algorithms are offline models and are not suitable for simulating the complex dynamic process of dam displacement. In addition, dam safety monitoring should quantify the uncertainty of the time-dependent displacement and establish reliable prediction intervals. As a result, an online learning model that can qualify prediction intervals is necessary. In this paper, an online learning algorithm named AF-OS-ELM-MVE is proposed to solve the problems. It is based on the OS-ELM algorithm and optimized using the adaptive forgetting factor mechanism. Through selective forgetting for parameter tuning, it achieves self-adaptation to the underlying physical mechanism between the factors and the displacement data. Mean Variance Estimation (MVE) is also adapted to establish the new hybrid model. It can be used to estimate the variance of the prediction process, and the model can generate prediction intervals based on the minimum probability theory. The AF-OS-ELM-MVE model is verified using long-term monitoring data and the performance comparisons were made with a number of state-of-the-art models. The correlation coefficients of the proposed model in point prediction are above 0.99 for measuring points of different elevations, and at a 95% confidence level, it achieves 100% coverage of real displacements. The results demonstrate the effectiveness of the proposed model in predicting dam deformations and provide a more solid basis for dam safety analysis.

Data-Driven Control Based on Information Concentration Estimator and Regularized Online Sequential Extreme Learning Machine

Due to the complexity of digital equipment and systems, it is quite difficult to obtain a precise mechanism model in practice. For an unknown discrete-time nonlinear system, in this paper, a semi-parametric model is used to describe this discrete-time nonlinear system, and this semi-parametric model contains a parametric uncertainty part and a nonparametric uncertainty part. Based on this semi-parametric model, a novel data-driven control algorithm based on an information concentration estimator and regularized online sequential extreme learning machine (ReOS-ELM) is designed. The information concentration estimator estimates the parametric uncertainty part; The training data of ReOS-ELM network is obtained, based on symmetry and information concentration estimator, then the training of ReOS-ELM network and the estimate of nonparametric uncertainty part using ReOS-ELM network are carried out online, successively. A stability analysis and three simulation examples were performed, and the simulation results show that the proposed data-driven control algorithm is effective in improving the control accuracy.

Real-Time Monitoring of Mental Fatigue of Construction Workers Using Enhanced Sequential Learning and Timeliness

The demanding nature of construction works exposes workers to prolonged physical labor and high-risk environments, increasing their vulnerability to mental fatigue and consequently posing risks to safety and productivity. Over the years, a number of mental fatigue monitoring models have been created based on the captured vital signs. However, these models are basically based on batch learning algorithms such as machine learning or deep learning, which are time-consuming and costly, and fail to consider the timeliness features embedded in the biological data. Aiming at time-varying vital signs, a novel regularized online sequential extreme learning machine with dynamic forgetting factor (ROSELM-DFF) model was established to realize the real-time monitoring of workers' mental fatigue. A cognitive experiment has been carried out and the results indicate that the proposed OSELM-DFF model outperforms other models in terms of computational efficiency and prediction accuracy, achieving the best Average Accuracy of 96.106%. This study offers an effective solution for proactive management of workers' mental fatigue, which is expected to foster a safer and more productive construction environment.

Short-term forecasting approach of single well production based on multi-intelligent agent hybrid model.

The short-term prediction of single well production can provide direct data support for timely guiding the optimization and adjustment of oil well production parameters and studying and judging oil well production conditions. In view of the coupling effect of complex factors on the daily output of a single well, a short-term prediction method based on a multi-agent hybrid model is proposed, and a short-term prediction process of single well output is constructed. First, CEEMDAN method is used to decompose and reconstruct the original data set, and the sliding window method is used to compose the data set with the obtained components. Features of components by decomposition are described as feature vectors based on values of fuzzy entropy and autocorrelation coefficient, through which those components are divided into two groups using cluster algorithm for prediction with two sub models. Optimized online sequential extreme learning machine and the deep learning model based on encoder-decoder structure using self-attention are developed as sub models to predict the grouped data, and the final predicted production comes from the sum of prediction values by sub models. The validity of this method for short-term production prediction of single well daily oil production is verified. The statistical value of data deviation and statistical test methods are introduced as the basis for comparative evaluation, and comparative models are used as the reference model to evaluate the prediction effect of the above multi-agent hybrid model. Results indicated that the proposed hybrid model has performed better with MAE value of 0.0935, 0.0694 and 0.0593 in three cases, respectively. By comparison, the short-term prediction method of single well production based on multi-agent hybrid model has considerably improved the statistical value of prediction deviation of selected oil well data in different periods. Through statistical test, the multi-agent hybrid model is superior to the comparative models. Therefore, the short-term prediction method of single well production based on a multi-agent hybrid model can effectively optimize oilfield production parameters and study and judge oil well production conditions.

A progressive learning for structural tolerance online sequential extreme learning machine

A progressive learning for structural tolerance online sequential extreme learning machine

Compliant Grasping Control for a Tactile Self-Sensing Soft Gripper.

Soft grippers with good passive compliance can effectively adapt to the shape of a target object and have better safe grasping performance than rigid grippers. However, for soft or fragile objects, passive compliance is insufficient to prevent grippers from crushing the target. Thus, to complete nondestructive grasping tasks, precision force sensing and control are immensely important for soft grippers. In this article, we proposed an online learning self-tuning nonlinearity impedance controller for a tactile self-sensing two-finger soft gripper so that its grasping force can be controlled accurately. For the soft gripper, its grasping force is sensed by a liquid lens-based optical tactile sensing unit that contains a self-sensing fingertip and a liquid lens module and has many advantages of a rapid response time (about 0.04 s), stable output, good sensitivity (>0.4985 V/N), resolution (0.03 N), linearity (R2 > 0.96), and low cost (power consumption: 5 mW, preparation cost <CNY 100). The proposed force controller for the soft gripper was designed based on the Hammerstein nonlinear model, and due to adaptive laws designed by an adaptive theory and the full online sequential extreme learning machine, respectively, its parameters can be adjusted online. The simulation and experiment results demonstrate that the proposed force controller exhibits good force control performance and robustness in a nonlinear contact environment. Moreover, because of its simple control structure and good online learning ability, the proposed controller has the advantages of being real time and easy to realize, which shows its potential applications in many grasping tasks, such as collecting biological samples and sorting industrial products.

Weld Quality Diagnosis of Gas Metal Arc Welding Based on Variational Mode Decomposition, Fuzzy Entropy, and Online Sequential Extreme Learning Machine

Weld Quality Diagnosis of Gas Metal Arc Welding Based on Variational Mode Decomposition, Fuzzy Entropy, and Online Sequential Extreme Learning Machine

Online semi-supervised learning for motor imagery EEG classification

ObjectiveTime-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated. ApproachWe proposed an online semi-supervised learning scheme to improve the classification performance of MI-based BCI. This scheme uses regularized weighted online sequential extreme learning machine (RWOS-ELM) as the base classifier and updates its model parameters with incoming balanced data chunk-by-chunk. In the initial stage, we designed a technique that combines the synthetic minority oversampling with the edited nearest neighbor rule for data augmentation to construct more discriminative initial classifiers. When used online, the incoming chunk of data is first pseudo-labeled by RWOS-ELM as well as an auxiliary classifier, and then balanced again by the above-mentioned technique. Initial classifiers are further updated based on these class-balanced data. Main resultsOffline experimental results on two publicly available MI datasets demonstrate the superiority of the proposed scheme over its counterparts. Further online experiments on six subjects show that their BCI performance gradually improved by learning from incoming unlabeled data. SignificanceOur proposed online semi-supervised learning scheme has higher computation and memory usage efficiency, which is promising for online MI-based BCIs, especially in the case of insufficient labeled training data.

Uncalibrated visual servoing based on Kalman filter and mixed-kernel online sequential extreme learning machine for robot manipulator

Uncalibrated visual servoing based on Kalman filter and mixed-kernel online sequential extreme learning machine for robot manipulator

On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

On-line evolutionary identification technology for milling chatter of thin walled parts based on the incremental-sparse K-means and the online sequential extreme learning machine

The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.

Two-Direction Prediction Method of Drilling Fluid Based on OS-ELM for Water Well Drilling

In this study, a drilling fluid prediction method based on an online sequential extreme learning machine (OS-ELM) is proposed, which is prepared for water well drilling on the muddy clay formation of Tarim Basin, Qinghai Province. First, we investigated the mechanism linking mix ratio to fluid performance, allowing us to employ an OS-ELM algorithm derived from the extreme learning machine. Particularly, the proposed prediction method is bidirectional to identify an appropriate slurry formulation. The forward prediction model is established to predict the fluid performance, where the mud additive contents are inputs, and the drilling fluid properties parameters are outputs. Correspondingly, the backward prediction model is established to modify the slurry formula, where differences in the drilling fluid properties are inputs and percentages of slurry additives amount are output. The simulation results show that the two-direction OS-ELM prediction model can better predict the drilling fluid properties in water well drilling.

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment and can help to lessen the negative effects of climate change by lowering the level of emissions of greenhouse gas. This study developed thirteen different artificial intelligence models, including multivariate adaptive regression splines (MARS), extreme learning machine (ELM), Kernel extreme learning machine (KELM), online sequential extreme learning machine (OSELM), optimally pruned extreme learning machine (OPELM), outlier robust extreme learning machine (ORELM), deep extreme learning machine (DELM), and their versions combined with variational mode decomposition (VMD) as integrated models (VMD-DELM, VMD-ORELM, VMD-OPELM, VMD-OSELM, VMD-KELM, and VMD-ELM), for solar radiation estimation in Kurdistan region, Iraq. The daily meteorological data from 2017 to 2018 were used to implement suggested artificial models at Darbandikhan and Dukan stations, Iraq. The input parameters included daily data for maximum temperature (MAXTEMP), minimum temperature (MINTEMP), maximum relative humidity (MAXRH), minimum relative humidity (MINRH), sunshine duration (SUNDUR), wind speed (WINSPD), evaporation (EVAP), and cloud cover (CLOUDCOV). The results show that the proposed VMD-DELM algorithm considerably enhanced the simulation accuracy of standalone models’ daily solar radiation prediction, with average improvement in terms of RMSE of 13.3%, 20.36%, 25.1%, 27.1%, 34.17%, 38.64%, and 48.25% for Darbandikhan station and 5.22%, 10.01%, 10.26%, 21.01%, 29.7%, 35.8%, and 40.33% for Dukan station, respectively. The outcomes of this study reveal that the VMD-DELM two-stage model performed superiorly to the other approaches in predicting daily solar radiation by considering climatic predictors at both stations.

An adaptive on-board real-time model with residual online learning for gas turbine engines using adaptive memory online sequential extreme learning machine

The on-board real-time model (ORM) of gas turbine engines (GTEs) is widely used in various applications of control systems, such as sensor fault-tolerant control and model-based control. However, the accuracy of the ORM in the primary stage is affected by changes in engine performance, including degradation and sudden changes, which inevitably occur during engine service. To address this issue, we propose an adaptive on-board real-time model (AORM) modelling framework for GTEs based on a component-level model (CLM) and a residual online learning model (ROLM). The CLM is constructed using classical component-level modelling theory, while the ROLM is built using the proposed adaptive memory online sequence extreme learning machine (AMOSELM) algorithm. The AMOSELM algorithm can effectively learn the residuals between the engine and the CLM during engine service, improving the accuracy of the model. Specifically, we compensate for the CLM by using the predicted residuals based on the AMOSELM to achieve enhanced accuracy in the AORM. The memory coefficient of the AMOSELM is adjusted using a bell-type membership function in the online learning stage. The shape parameters of the membership function are updated using an off-line optimization strategy based on particle swarm optimization when the online learning is finished. To validate the effectiveness of the AORM framework and the AMOSELM algorithm, we conducted both virtual engine flight data simulation and actual ground test verification. The verification results show that the AMOSELM algorithm can effectively realize residual online learning during engine service, and performs better than other traditional online learning algorithms. Moreover, using the predicted residuals to compensate for the CLM can significantly reduce the modelling error of the AORM, achieving better modelling accuracy.

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone.

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things.

Hybrid intelligent deep learning model for solar radiation forecasting using optimal variational mode decomposition and evolutionary deep belief network - Online sequential extreme learning machine

Accurate prediction of solar radiation is of great significance to improve the utilization of solar energy for photovoltaic power generation on the roofs of urban buildings. Therefore, a hybrid model, namely the OVMD-PACF-ISSA-DBN-OSELM model, is being proposed in this study for solar radiation prediction. Firstly, the data is decomposed by optimal variational mode decomposition (OVMD) technology. Secondly, the partial autocorrelation function (PACF) technology selected features for components decomposed by OVMD technology. Thirdly, a deep belief network-online sequential extreme learning machine (DBN-OSELM) prediction model is constructed, and sparrow search algorithm (SSA) is utilized to optimize the model hyperparameters. Aiming at the shortcomings of SSA algorithm, Tent map and differential evolution strategy are introduced to improve the SSA algorithm. Finally, the model is used to predict each component, and the prediction results of each component are summarized to obtain the prediction results of solar radiation. To verify that the proposed method can obtain accurate prediction results, experiments are conducted using solar radiation data for January, May and October 2022, and compared with other models. Taking January as an example, compared with shallow model BP, the RMSE, MAE and R of the proposed model are increased by 66.83%, 58.65% and 5.33%, respectively. Moreover, the experiments of the proposed model in other months have excellent results. The current findings demonstrate that the suggested model is a strong contender in the arena of solar radiation prediction, and can consequently provide valuable support in enhancing the efficiency of photovoltaic equipment installed on urban building roofs.

A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM

Rolling bearings are critical in maintaining smooth operation of rotating machinery and considerably influence its reliability. The signals collected from rolling bearings in field conditions are often subjected to noise, creating a challenge to extract weaker fault features. This paper proposes a rolling bearing fault diagnosis method that addresses the above-mentioned problem through the moth-flame optimization algorithm optimized variational mode decomposition (MFO-optimized VMD) and an ensemble differential evolution online sequential extreme learning machine (DE-OSELM). By using the dynamic adaptive weight factor and genetic algorithm cross operator, the optimization accuracy and global optimization ability of the moth-flame optimization (MFO) are improved, and the two basic parameters of VMD decomposition level and quadratic penalty factor are adaptive selected. Since the vibration characteristics of the signal cannot be fully interpreted by a single index, The effective weighted correlation sparsity index (EWCS) is utilized to extract the relevant intrinsic mode functions (IMF) of VMD decomposition and extract their energies as features. In order to improve the classification accuracy, The energy feature set is subsequently inputted into DE-OSELM for training and classification purposes, and the proposed method is assessed via a sample set with four different health states of actual rolling bearings. Our proposed method results are compared with other diagnosis methods, proving its feasibility to diagnose rolling bearing faults with higher classification accuracy.

A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks.

This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.