Particle Swarm Optimization Extreme Learning Research Articles

A Mine Water Source Prediction Model Based on LIF Technology and BWO-ELM.

The traditional methods for identifying water sources in coal mines lack the ability to quickly detect water sources and are prone to causing secondary pollution of samples. In contrast, laser induced fluorescence (LIF) technology has been introduced for the identification of coal mine water sources due to its high sensitivity and real-time performance. However, extreme learning machine (ELM) have shortcomings in randomly selecting weights and biases. The Beluga Whale Optimization (BWO) algorithm has efficient optimization capability, global search capability, adaptability and parallelism, and can find the optimal weights and biases in a short time. The combination of LIF technology and BWO-ELM model can be applied to quickly identify the welling water source in coal mine. Select sandstone water and old goaf water from the Huainan mining area as experimental samples, and mix them in different proportions to prepare 7 mixed water samples for testing. Utilize LIF technology to obtain spectral curve images, preprocess them with polynomial smoothing algorithm (SG) and spectral multiple scattering correction (MSC), and perform dimensionality reduction using factor analysis (FA) and linear discriminant analysis (LDA) methods. Finally, construct ELM models, Long Short Term Memory (LSTM) models, BWO-ELM models, and Particle Swarm Optimization Extreme Learning Machine(PSO-ELM) models for the dimensionality reduced data. In order to improve the reliability and accuracy of the results, the experimental results were kept to 5 decimal places. From the experimental results, it can be seen that SG-LDA-BWO-ELM has the best fitting effect, with a fitting coefficient of 0.99990, a root mean square error of 0.00041, a mean square error approaching 0, and an average absolute error of 0.00021. It has the best convergence and the smallest absolute error among all models, making it the most suitable for identifying mine water inrush. It is of great significance for preventing and controlling mine water disasters and ensuring coal mine production safety.

Construction of a Winter Wheat Comprehensive Growth Monitoring Index Based on a Fuzzy Degree Comprehensive Evaluation Model of Multispectral UAV Data.

Realizing real-time and rapid monitoring of crop growth is crucial for providing an objective basis for agricultural production. To enhance the accuracy and comprehensiveness of monitoring winter wheat growth, comprehensive growth indicators are constructed using measurements of above-ground biomass, leaf chlorophyll content and water content of winter wheat taken on the ground. This construction is achieved through the utilization of the entropy weight method (EWM) and fuzzy comprehensive evaluation (FCE) model. Additionally, a correlation analysis is performed with the selected vegetation indexes (VIs). Then, using unmanned aerial vehicle (UAV) multispectral orthophotos to construct VIs and extract texture features (TFs), the aim is to explore the potential of combining the two as input variables to improve the accuracy of estimating the comprehensive growth indicators of winter wheat. Finally, we develop comprehensive growth indicator inversion models based on four machine learning algorithms: random forest (RF); partial least squares (PLS); extreme learning machine (ELM); and particle swarm optimization extreme learning machine (PSO-ELM), and the optimal model is selected by comparing the accuracy evaluation indexes of the model. The results show that: (1) The correlation among the comprehensive growth indicators (CGIs) constructed by EWM (CGIewm) and FCE (CGIfce) and VIs are all improved to different degrees compared with the single indicators, among which the correlation between CGIfce and most of the VIs is larger. (2) The inclusion of TFs has a positive impact on the performance of the comprehensive growth indicator inversion model. Specifically, the inversion model based on ELM exhibits the most significant improvement in accuracy. The coefficient of determination (R2) values of ELM-CGIewm and ELM- CGIfce increased by 20.83% and 20.37%, respectively. (3) The CGIfce inversion model constructed by VIs and TFs as input variables and based on the ELM algorithm is the best inversion model (ELM-CGIfce), with R2 reaching 0.65. Particle swarm optimization (PSO) is used to optimize the ELM-CGIfce (PSO-ELM-CGIfce), and the precision is significantly improved compared with that before optimization, with R2 reaching 0.84. The results of the study can provide a favorable reference for regional winter wheat growth monitoring.

APO-ELM Model for Improving Azimuth Correction of Shipborne HFSWR

Shipborne high-frequency surface wave radar (HFSWR) has a wide range of applications and plays an important role in moving target detection and tracking. However, the complexity of the sea detection environment causes the target signals received by shipborne HFSWR to be seriously disturbed by sea clutter. Sea clutter increases the difficulty of azimuth estimation, resulting in a challenging problem for shipborne HFSWR. To solve this problem, a novel azimuth correction method based on adaptive boosting error feedback dynamic weighted particle swarm optimization extreme learning machine (APO-ELM) is proposed to improve the azimuth estimation accuracy of shipborne HFSWR. First, the sea clutter is modeled and simulated. Then, we study its characteristics and analyze the influence of its characteristics on the first-order clutter spectrum and target detection accuracy, respectively. In addition, the proposed improved particle swarm optimization (PSO) and adaptive neuron clipping algorithm are used to optimize the input parameters of the ELM network. Then, the network performs error feedback based on the optimized parameter performance and updates the feature matrix, which can give a minimum clutter-error estimation. After that, it iteratively trains multiple weak learners using the adaptive boosting (AdaBoost) algorithm to form a strong learner and make strong predictions. Finally, after error compensation, the best azimuth estimation results are obtained. The sample sets used for the APO-ELM network are obtained from field shipborne HFSWR data. The network training and testing features include the wind direction, sea current, wind speed, platform speed, and signal-to-clutter ratio (SCR). The experimental results show that this method has a lower root-mean-square error than the back-propagation neural network and support vector regression (SVR) azimuth correction methods, which verifies the effectiveness of the proposed method.

Development of the thermophysical parameter tester for measuring the soil matrix suction and optimization of the calibration function

The accurate measurement of the soil matrix suction is the prerequisite for understanding the mechanism of unsaturated soil. In this study, the in-situ thermophysical parameter tester was developed for measuring the soil matrix suction. The determination of the soil–water characteristic curve (SWCC) is a key step in the determination of the calibration function. Therefore, the simulation performances of three theoretical models and three machine learning models on SWCC were compared. It was concluded that the particle swarm optimization extreme learning machine (PSO-ELM) model outperforms the other models. The calibration function was obtained by combining the PSO-ELM model with the line heat source theory, and the coefficient of determination (R2) of the calibration model was greater than 0.95. The calibration function was then applied to a field test in order to verify the performance of the designed instrument and compare it with the tensiometer. Finally, the error propagation and synthesis theory based on random error was adopted, and it was deduced that the theoretical systematic error of the instrument is 9.85 %. Considering that the tensiometer also has a test error, it is believed that the relative measurement error of the two instruments is reasonable. Therefore, the instrument designed with the optimal calibration function can effectively measure the soil matrix suction. It also allows to determine the temperature, thermal conductivity, and thermal diffusion coefficient, providing an accurate measurement method for the soil matrix suction.

Initial-Productivity Prediction Method of Oil Wells for Low-Permeability Reservoirs Based on PSO-ELM Algorithm

Conventional numerical solutions and empirical formulae for predicting the initial productivity of oil wells in low-permeability reservoirs are limited to specific reservoirs and relatively simple scenarios. Moreover, the few influencing factors are less considered and the application model is more ideal. A productivity prediction method based on machine learning algorithms is established to improve the lack of application performance and incomplete coverage of traditional mathematical modelling for productivity prediction. A comprehensive analysis was conducted on the JY extra-low-permeability oilfield, considering its geological structure and various factors that may impact its extraction and production. The study collected 13 factors that influence the initial productivity of 181 wells. The Spearman correlation coefficient, ReliefF feature selection algorithm, and random forest selection algorithm were used in combination to rank the importance of these factors. The screening of seven main controlling factors was completed. The particle swarm optimization–extreme learning machine algorithm was adopted to construct the initial-productivity model. The primary control factors and the known initial productivity of 127 wells were used to train the model, which was then used to verify the initial productivity of the remaining 54 wells. In the particle swarm optimization–extreme learning machine (PSO-ELM) algorithm model, the root-mean-square error (RMSE) is 0.035 and the correlation factor (R2) is 0.905. Therefore, the PSO-ELM algorithm has a high accuracy and a fast computing speed in predicting the initial productivity. This approach will provide new insights into the development of initial-productivity predictions and contribute to the efficient production of low-permeability reservoirs.

A spatiotemporal model for PM2.5 prediction based on the K‐Core idea and label distribution

AbstractWith the increasingly severe problem of PM2.5 environmental pollution, the threat to human health is gradually increasing. Therefore, accurate prediction of PM2.5 concentration is of great significance to the healthy life of human beings. To make up for the deficiencies of previous studies on PM2.5 concentration prediction, a spatiotemporal model (Spatiotemporal prediction model of label distribution, LDSPM) for PM2.5 concentration prediction based on the K‐Core algorithm concept and label distribution learning was proposed. Leveraging K‐Core ideas and the label distribution support vector regression model of the label distribution paradigm, the influence weight of each meteorological factor on PM2.5 concentration in each piece of data was obtained with the decomposition of meteorological factors using the complete ensemble empirical mode decomposition of adaptive noise. Using a long short‐term memory neural network to predict each decomposed signal and obtain the forecast data of meteorological factors. Finally, according to the expected weight and meteorological factor data, a particle swarm optimization extreme learning machine is used to train the prediction, and the predicted value of PM2.5 is obtained. The experimental results show that the forecasting model performs better than other combined and single forecasting models. It provides new directions and ideas for PM2.5 concentration prediction.

Classification of English Translation Teaching Models based on Multiple Intelligence Theory

In order to improve the quality of English translation teaching, this paper combines the theory of multiple intelligence to classify the English translation teaching process. Moreover, this paper adopts Fisher’s discriminant method and Bayesian discriminant method to classify the English translation teaching samples. In order to improve the discrimination accuracy of the extreme learning machine algorithm, this paper applies the particle swarm optimization extreme learning machine algorithm to the research on the classification of English translation teaching samples and proposes an intelligent English classification teaching model based on the actual situation of English translation teaching. In addition, this paper verifies the system model proposed in this paper by evaluating the teaching method. The research shows that the classification model of English translation teaching mode based on the theory of multiple intelligence proposed in this paper has a certain effect, which can promote the effect of English translation teaching.

Improved Particle Swarm Optimization-Extreme Learning Machine Modeling Strategies for the Accurate Lithium-ion Battery State of Health Estimation and High-adaptability Remaining Useful Life Prediction

To ensure the secure and stable operation of lithium-ion batteries, the state of health (SOH) and the remaining useful life (RUL) are the critical state parameters of lithium-ion batteries, which need to be estimated precisely. A joint SOH and RUL estimation approach based on an improved Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) is proposed in this paper. The approach adopts Pearson coefficients to screen multivariate information of the discharge process as health indicators and uses them as inputs to enable accurate estimation of SOH and RUL prediction of lithium-ion batteries on the basis of the PSO-ELM model. The validity of the model is demonstrated by the NASA lithium-ion battery data set: the maximum root mean square error (RMSE) of the SOH estimation of the tested battery is 0.0033, the maximum RMSE of its RUL prediction is 0.0082, and the maximum absolute error of RUL prediction is one cycle number. In comparison with the prediction results of the traditional extreme learning machine, the optimized model proposed in this paper estimates the SOH of lithium-ion batteries and RUL with relatively high accuracy.

A novel approach for estimating the capacity of ungauged small reservoirs using remote sensing and DEM

Abstract The estimation of small reservoir capacity is of great significance for water resources management. However, many widely distributed small reservoirs lack the capacity information because of the high costs of field measurements. This study proposed a novel approach to estimate the small reservoir capacity in the hilly area by using remote sensing and Digital Elevation Model (DEM). The basic idea of this approach is to explore the relationship between influential factors (i.e., topographic and geomorphic parameters) and measured reservoirs’ capacity to establish a machine learning model based on particle swarm optimization–extreme learning machine (PSO–ELM) to estimate the capacity. The Mihe River basin in northern China is selected as a case study, 111 measured reservoirs, and six optional influential factors are selected to develop and test this model. The results show that the five influential factors (i.e., the area of sub-catchment, the water surface area, the longest flow path of sub-catchment, the average slope of sub-catchment, and the average slope of buffer area) are the optimal combination with the lowest difference between the measured and the estimated reservoir capacities. The results demonstrate that the proposed approach is a robust tool for estimating the capacity of small reservoirs in the hilly area.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm.

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.

A comparative study of different machine learning methods for reservoir landslide displacement prediction

This paper compares the performance of five popular machine learning methods, namely, particle swarm optimization–extreme learning machine (PSO–ELM), particle swarm optimization–kernel extreme learning machine (PSO–KELM), particle swarm optimization–support vector machine (PSO–SVM), particle swarm optimization–least squares support vector machine (PSO–LSSVM), and long short-term memory neural network (LSTM), in the prediction of reservoir landslide displacement. The Baishuihe, Shuping, and Baijiabao landslides in the Three Gorges reservoir area of China were used for case studies. Cumulative displacement was decomposed into trend displacement and periodic displacement by the Hodrick–Prescott filter. The double exponential smoothing method and the five machine learning methods were used to predict the trend and periodic displacement, respectively. The five machine learning methods are compared in three aspects: highest single prediction accuracy, mean prediction accuracy, and prediction stability. The results show that no method performed the best for all three aspects in the three landslide cases. LSTM and PSO–ELM achieved better single prediction accuracy, but worse mean prediction accuracy and stability. PSO–KELM, PSO–LSSVM, and PSO–SVM always yielded consistent predictions with slight variations. On the whole, PSO–KELM and PSO–LSSVM are recommended for their superior mean prediction accuracy and prediction stability.

Remote Sensing Image Recognition Based on LOG-T-SSA-LSSVM and AE-ELM Network.

Aiming at the influence of different working conditions on recognition accuracy in remote sensing image recognition, this paper adopts hierarchical strategy to construct a network. Firstly, in order to establish the classification relationship between different samples, labeled samples are used for classification. A Logistic-T-distribution-Sparrow Search Algorithm-Least Squares Support Vector Machines (LOG-T-SSA-LSSVM) classification network is proposed. LOG-T-SSA algorithm is used to optimize parameters in LSSVM to establish a better network to achieve accurate classification between sample sets and then identify according to different categories. Through UCI dataset test, the accuracy of LOG-T-SSA-LSSVM network classification is significantly improved compared with that of contrast network. The autoencoder is integrated with Extreme Learning Machine, and the autoencoder is used to realize data compression. The advantages of Extreme Learning Machine (ELM) network, such as less training parameters, fast learning speed, and strong generalization ability, are fully utilized to realize efficient and supervised recognition. Experiments verify that the autoencoder-extreme learning machine (AE-ELM) network has a good recognition effect when the sigmoid activation function is selected and the number of hidden layer neurons are 2000. Finally, after image recognition under different working conditions, it is proved that the recognition accuracy of AE-ELM based on LOG-T-SSA-LSSVM classification is significantly improved compared with traditional ELM network and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) network.

Classification of Rubber Vulcanizing Accelerators Based on Particle Swarm Optimization Extreme Learning Machine and Terahertz Spectra

Classification of Rubber Vulcanizing Accelerators Based on Particle Swarm Optimization Extreme Learning Machine and Terahertz Spectra

The non-excavation corrosion prediction model of grounding grid based on particle swarm optimization extreme learning machine

The grounding grid is an indispensible part in the eletrical system. Nevertheless, the grounding grid materials are susceptible. Considering the small sample size and strong nonlinear feature of the grounding grid corrosion, we introduced a non-excavation corrosion prediction model based on particle swarm optimization extreme learning machine. This model utilized the extreme learning machine to fast deal with the nonlinear relationship, and utilized the particle swarm optimization to search global optimal solution. Compared with generalized regression neural network and BP neural network, the prediction results of this model are more accurate. Thus, this model might have bright future in improving the accuracy of corrosion prediction of grounding grids.

Transformer Fault Diagnosis Based on RapidMiner and Modified ELM Algorithm

For transformer fault diagnosis, the three ratio method lacks of encoding, and artificial intelligence methods lack of anti-interference ability.Thus, a new method of transformer fault diagnosis based on RapidMiner and modified particle swarm optimization Extreme Learning Machine (RM-MPSO-ELM) is proposed. Firstly, RapidMiner picks out the most relevant input variables of the transformer fault. then, using the modified particle swarm algorithm to optimize the parameters for Extreme Learning Machine. Finally, using the ELM to identify the potential of transformer fault, the diagnostic performance of IEC three ratio method, support vector machine (SVM) method and different combinations of ELM algorithm are also compared. The results show that the proposed method achieves higher diagnosis precision.

Classification of Tree Species at the Leaf Level based on Hyperspectral Imaging Technology

This study utilized hyperspectral imaging technology to identify eight tree species at the leaf level. The successive projections algorithm (SPA), information gain (IG), and Gini index (Gini) were used to select the feature bands. Furthermore, the binary particle swarm optimization (BPSO) algorithm was used to optimize the feature bands selected by SPA, IG, and Gini. The particle swarm optimization–extreme learning machine (PSO–ELM), linear Bayes normal classifi er (LBNC), and k-nearest neighbor (KNN) recognition models for tree species were established based on all bands, feature bands, and optimized feature bands, respectively. The experimental results show that the recognition rates of the PSO–ELM, LBNC, and KNN models based on all bands were 98.45, 99.10, and 83.67%, respectively. The SPA, IG, and Gini models can all effectively select spectral bands on tree species discrimination and greatly reduce the dimension of spectral data, in which the recognition effects of the models based on the feature bands selected by Gini were the best, and the recognition rates of the PSO–ELM, LBNC, and KNN models reached 97.55, 96.53, and 80.5%, respectively. Additionally, BPSO–SPA, BPSO–IG, and BPSO–Gini models can all further reduce the dimension of spectral data on the basis of ensuring the recognition accuracy of models, in which the models established based on the optimized feature bands selected by BPSO–Gini achieved the best recognition effect and the recognition rates of the PSO–ELM, LBNC, and KNN models reached 96.53, 96.68, and 81.05%, respectively. In general, the recognition performance of the PSO–ELM model was better than those of the LBNC and KNN models.

Peak Load Forecasting for Electrical Bus based on Limited Historical Data under Complex Meteorological Condition

The historical data of peak load for electrical bus are limited and fluctuated violently. The fluctuation ability of peak load for electrical bus is nonlinear and randomly. So its prediction accuracy is low. In order to improve the accuracy of peak load forecasting for electrical bus, a peak load forecasting for electrical bus method based on limited historical data under complex weather conditions is proposed. Firstly, the influence of natural meteorology, society and other factors on peak load fluctuation for electrical bus is analysed; Secondly, based on the reduction of redundancy between features of potential feature set, the feature importance ranking is obtained by conditional mutual information (CMI). Then, according to the improved particle swarm optimization extreme learning machine suitable for small sample training, the forward feature selection is performed to determine the optimal feature subset. Finally, based on the optimal feature subset, the optimal peak load forecasting model is established.

Ultra-Short-Term Wind Power Prediction by Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine

Wind power generation accounts for an increasing proportion of the power grid, so efficient and accurate real-time wind power prediction is particularly important for wind power grid. In view of the strong randomness and fluctuation of wind and the difficulty of predicting wind power, a Salp Swarm Algorithms-Extremely Learning Machine (SSA-ELM) based ultra-short-term wind power prediction model is proposed. In this case, the multi-input sample set is composed of historical wind speed, temperature, wind direction, atmospheric pressure and other climatic factors that are highly correlated with wind power, and the network parameters are determined in the training process. In order to improve the adaptability and accuracy of the prediction model, the input weight matrix and hidden layer deviation of the Extreme Learning Machine (ELM) are optimized by exploring and developing the Salp Swarm Algorithm in the iterative process. Finally, the simulation experiment is conducted with the actual data of a wind farm in Henan Province, and the comparison with the traditional Extreme Learning Machine, Particle Swarm Optimization Extreme Learning Machine (PSO-ELM) and Back Propagation (BP) neural network model shows that the new method avoids falling into the local extreme value, and has faster convergence speed and higher prediction accuracy.

Development of online classification system for construction waste based on industrial camera and hyperspectral camera.

Construction waste is a serious problem that should be addressed to protect environment and save resources, some of which have a high recovery value. To efficiently recover construction waste, an online classification system is developed using an industrial near-infrared hyperspectral camera. This system uses the industrial camera to capture a region of interest and a hyperspectral camera to obtain the spectral information about objects corresponding to the region of interest. The spectral information is then used to build classification models based on extreme learning machine and resemblance discriminant analysis. To further improve this system, an online particle swarm optimization extreme learning machine is developed. The results indicate that if a near-infrared hyperspectral camera is used in conjunction with an industrial camera, construction waste can be efficiently classified. Therefore, extreme learning machine and resemblance discriminant analysis can be used to classify construction waste. Particle swarm optimization can be used to further enhance the proposed system.

Dolphin Swarm Extreme Learning Machine

As a novel learning algorithm for a single hidden-layer feedforward neural network, the extreme learning machine has attracted much research attention for its fast training speed and good generalization performances. Instead of iteratively tuning the parameters, the extreme machine can be seen as a linear optimization problem by randomly generating the input weights and hidden biases. However, the random determination of the input weights and hidden biases may bring non-optimal parameters, which have a negative impact on the final results or need more hidden nodes for the neural network. To overcome the above drawbacks caused by the non-optimal input weights and hidden biases, we propose a new hybrid learning algorithm named dolphin swarm algorithm extreme learning machine adopting the dolphin swarm algorithm to optimize the input weights and hidden biases efficiently. Each set of input weights and hidden biases is encoded into one vector, namely the dolphin. The dolphins are evaluated by root mean squared error and updated by the four pivotal phases of the dolphin swarm algorithm. Eventually, we will obtain an optimal set of input weights and hidden biases. To evaluate the effectiveness of our method, we compare the proposed algorithm with the standard extreme learning machine and three state-of-the-art methods, which are the particle swarm optimization extreme learning machine, evolutionary extreme learning machine, and self-adaptive evolutionary extreme learning machine, under 13 benchmark datasets obtained from the University of California Irvine Machine Learning Repository. The experimental results demonstrate that the proposed method can achieve superior generalization performances than all the compared algorithms.