Number Of Nodes In Hidden Layer Research Articles

การพยากรณ์ยอดขายและความต้องการสินค้าในอุตสาหกรรมผลิตอุปกรณ์ไฟฟ้า กรณีศึกษา บริษัทผลิตอุปกรณ์ไฟฟ้าแรงสูง

The purpose of this study is to find the product demand forecasting model from the artificial neural network method in a case study of a high-voltage equipment manufacturing company. And reduce fines for late delivery of goods by using the Matlab program. The forecasting model is established by using time series neural network method, and the nonlinear autoregressive neural network (NARX) function model is established by combining with external input data. Then the forecast deviation is the average deviation percentage error (MAPE) uses the least dynamics model. By using data that collects product sales from 2019 – 2022 total 48 set of data are divided into 2 data sets used in the study. by the first dataset is learning 36 datasets and the second dataset is learning 12 datasets. The model of this study was defined with the number of hidden layer nodes as 2, 6 and 10. The time delay was 2 and 3. Forecasting models are combined into 6 models. The best model result of each item from NARX forecast through the learning process Levenberg-Marquardt (trainlm), including models 1, 4, 5, 4, 3, 3, and 2 sequential The results show the least average absolute percentage error at 8.32, 5.85, 2.61, 3.39, 2.91, 2.19 and 7.76 and the results of the forecasting model were analyzed to reduce fines from selling all 7 items Able to reduce penalties for late delivery of 4 items showing the percentage of difference in cost reduction equal to 0.28% 0.24% 0.32% and 0.22%. It can be concluded that the prediction of the neural network inverted neural network model It can be used to forecast product sales and product demand as well. And reduce the cost of late delivery fines for better results.

A modeling approach for the absorber of absorption heat pump systems

The study on the data-and-knowledge-driven modeling methods is very important for the digital twinning and the optimal control of absorption heat pump systems. For the purpose of obtaining an absorber model with high accuracy, low computational effort and less time-consuming for the absorption heat pump systems, a modeling method is proposed by combining the Simple Particle Swarm Optimization (SPSO) and the Wavelet Neural Network (WNN). The input-output structure of the model is presented by analyzing the operational principles of the absorber. In view of the complexity of the heat transfer and mass transfer processes inside the absorber, a single hidden layer WNN is employed as the internal structure of the model. The number of hidden layer nodes in the network model is determined by the trial-and-error method using the root mean square error and coefficient of determination. To overcome the drawback of WNN which can easily fall into the local optima during the error backpropagation process and improve the prediction accuracy of the model, the Morlet wavelet basis function and a SPSO algorithm are exploited respectively to replace the S-function of Neural Network and determine the optimal parameter values of the neural network (e.g. the weight, scaling factor, and translation factor, etc.). Experimental simulation results show the root mean square errors of the proposed model are 1.59 % and 2.21 % for the two outputs, respectively, which are smaller than the conventional WNN model. The proposed modeling approach effectively solves the problems of the BP neural network on easy falling into the local optimum and the slow convergence, which will be widely used in the process of the optimized operation control for heat pump systems.

Effective fault module localization in substation critical equipment: an improved ant colony optimization and back propagation neural network approach

AbstractThe rapid development of substations has increased the demand for accurate and fast fault prediction systems. In order to achieve rapid localization and autonomous decision‐making of fault modules and types in substations, the article proposes a fault autonomous localization algorithm based on improved ant colony optimization (IACO) and back propagation neural network (BPNN). The fault data of the substation secondary equipment for training and testing the BPNN model is based on the actual operating equipment of the substation, which can significantly improve the reliability of the model results. In addition, the IACO is used to globally optimize the weights and thresholds of BPNN, and the number of hidden layer nodes in BPNN was analyzed to further improve the accuracy of the established fault prediction algorithm. The test results show that the fault prediction accuracy of the BPNN model optimized by IACO is 93.67%, which is significantly improved compared to the traditional BPNN and BPNN with ant colony optimization method (with an accuracy of 82.98% and 91.04%). The above results effectively demonstrate the high accuracy and effectiveness of the established prediction algorithm in processing data and locating faults, which can improve the maintenance and operational efficiency of substations.

The Successful Prediction Of Waterflooding Using A Feed Forward Algorithm

Waterflooding is one of the most frequently used EOR methods to increase oil recovery because it can increase 30% -60% of total production. It is necessary to apply a production system performance prediction approach to minimize uncertainty in increasing production figures, such as analytical methods and numerical methods. Artificial Intelligence in the world of oil and gas is not a new thing, but it has often been used in various fields such as exploration, drilling, production, and reservoirs. So this is the basis for the prediction of the success of waterflooding research carried out. The purpose of this research was to predict the success rate of waterflooding using an Artificial Neural Network (ANN). The method used in this study is the simulation research method using CMG Imex for reservoir simulation modeling, running CMG CMOST for 500 sensitivity data with the input of seven parameters of compressibility, horizontal permeability, vertical permeability, pressure injection, injection rate, thickness, oil saturation, and the output is recovery factor using Artificial Neural Network (ANN) with a ratio of 70% of the RF calculation model results for training and 30% model results for testing. In order to get optimal prediction results, trial, and error were carried out on the number of hidden layer nodes, so that optimal and stable hidden layer nodes were obtained at node 10 with RMSE values of 0.339035 for training and 0.442663 for testing and MAPE for training 1.15% and 1.62% for testing. The statistical analysis value is 0.906139 for training and 0.899525 for testing data. It can be concluded from this study that the use of ANN in predictions using ten hidden layer nodes proved to be very good and successful, and predictions in this study were classified as High Accurate Prediction.

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification.

The features of the kernel extreme learning machine-efficient processing, improved performance, and less human parameter setting-have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space-time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model's generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm's current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area

The accurate calculation of mining-induced surface deformation has important guiding significance for efficient and safe production in mining areas. The probability integral method (PIM) is a main prediction method in China, and the selection of its parameters is directly related to the prediction accuracy of surface deformation in mining areas. To overcome shortcomings of PIM and other methods, this paper proposed a prediction model of the parameters of PIM combining a multiple regression model and an extreme learning machine. In this paper, the Huainan mining area was selected as the research object, the influence factors of PIM parameters were analyzed and the accuracy of the model was verified. The influence of the number of hidden layer nodes, the selection of activation function and the proportion of training set and test set in the model were analyzed. The conclusions suggest that the PIM parameters calculated in this paper could be used to predict mining subsidence and obtain surface movement and deformation data. The research results provide an effective method for the selection of surface deformation prediction parameters of new working faces or faces lacking measured data.

Gearbox Fault Diagnosis Based on Optimized Stacked Denoising Auto Encoder and Kernel Extreme Learning Machine

The gearbox is one of the key components of many large mechanical transmission devices. Due to the complex working environment, the vibration signal stability of the gear box is poor, the fault feature extraction is difficult, and the fault diagnosis accuracy makes it difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the particle swarm optimization algorithm in adaptive weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that the classification accuracy of the proposed method can reach 97.2% under the condition of low signal-to-noise ratio, which shows the effectiveness and robustness of the proposed method compared with other diagnostic methods.

Prediction of the corrosion depth of oil well cement corroded by carbon dioxide using GA-BP neural network

A corrosion prediction model was established based on the genetic algorithm (GA) and back propagation (BP) neural network to predict the long-term corrosion changes of oil well cement, Considering that the cement sheath is susceptible to corrosion and its corrosion degree is not easy to observe in acid gas wells and geological storage wells containing carbon dioxide (CO2). The initial weights and thresholds of the neural network were optimized by GA. The number of hidden layer nodes was selected by error verification, and the network was trained with an improved algorithm. The sample data was regression processed based on the empirical formula and was used in the network training. The simulation results shows that: The improved GA-BP network model (3–5-6–1) has a higher prediction accuracy with faster convergence and better fitting effect compared with the traditional BP neural network and the regression model (REG) in long-term prediction of corrosion depth in oil well cement. The regression coefficient (R2) of the prediction model is 0.9913, and the mean square error (MSE) of test samples is 0.0026. The modeling idea proposed in this paper can be applied to improve the accuracy of prediction models in predicting the corrosion of oil well cement.

State-of-health estimation of Lithium-ion battery based on back-propagation neural network with adaptive hidden layer

The reliability and safety of lithium-ion batteries (LIBs) are key issues in battery applications. Accurate prediction of the state-of-health (SOH) of LIBs can reduce or even avoid battery-related accidents. In this paper, a new back-propagation neural network (BPNN) is proposed to predict the SOH of LIBs. The BPNN uses as input the LIB voltage, current and temperature, as well as the charging time, since it is strongly correlated with the SOH. The number of hidden layer nodes is adaptively set based on the training data in order to improve the generalization capability of the BPNN. The effectiveness and robustness of the proposed scheme is verified using four distinct battery datasets and different training data. Experimental results show that the new BPNN is able to accurately predict the SOH of LIBs, revealing superiority when compared to other alternatives.

STWD-SFNN: Sequential three-way decisions with a single hidden layer feedforward neural network

The three-way decisions strategy has been employed to construct network topology in a single hidden layer feedforward neural network (SFNN). However, this model has a general performance, and does not consider the process costs, since it has fixed threshold parameters. Inspired by the sequential three-way decisions (STWD), this paper proposes STWD with an SFNN (STWD-SFNN) to enhance the performance of networks on structured datasets. STWD-SFNN adopts multi-granularity levels to dynamically learn the number of hidden layer nodes from coarse to fine, and set the sequential threshold parameters. Specifically, at the coarse granular level, STWD-SFNN handles easy-to-classify instances by applying strict threshold conditions, and with the increasing number of hidden layer nodes at the fine granular level, STWD-SFNN focuses more on disposing of the difficult-to-classify instances by applying loose threshold conditions, thereby realizing the classification of instances. Moreover, STWD-SFNN considers and reports the process cost produced from each granular level. The experimental results verify that STWD-SFNN has a more compact network on structured datasets than other SFNN models, and has better generalization performance than the competitive models. All models and datasets can be downloaded from https://github.com/wuc567/Machine-learning/tree/main/STWD-SFNN.

A novel correlation Gaussian process regression-based extreme learning machine.

An obvious defect of extreme learning machine (ELM) is that its prediction performance is sensitive to the random initialization of input-layer weights and hidden-layer biases. To make ELM insensitive to random initialization, GPRELM adopts the simple an effective strategy of integrating Gaussian process regression into ELM. However, there is a serious overfitting problem in kernel-based GPRELM (kGPRELM). In this paper, we investigate the theoretical reasons for the overfitting of kGPRELM and further propose a correlation-based GPRELM (cGPRELM), which uses a correlation coefficient to measure the similarity between two different hidden-layer output vectors. cGPRELM reduces the likelihood that the covariance matrix becomes an identity matrix when the number of hidden-layer nodes is increased, effectively controlling overfitting. Furthermore, cGPRELM works well for improper initialization intervals where ELM and kGPRELM fail to provide good predictions. The experimental results on real classification and regression data sets demonstrate the feasibility and superiority of cGPRELM, as it not only achieves better generalization performance but also has a lower computational complexity.

A Back Propagation Neural Network-Based Radiometric Correction Method (BPNNRCM) for UAV Multispectral Image

Radiometric correction is one of the most important preprocessing parts of unmanned aerial vehicle (UAV) multispectral remote sensing data analysis and application. In this article, a back propagation (BP) neural network-based radiometric correction method (BPNNRCM) considering optimal parameters was proposed. First, we used different UAV multispectral sensors (K6 equipped on the DJI M600, D-MSPC2000 equipped on the FEIMA D2000) to collect training, validation, testing and cross-validation data. Second, the radiometric correction results of BP neural network with different input variables and hidden layer node number were compared to select the best combination of input parameters and hidden layer node number. Finally, the radiometric correction accuracy and robustness of BP neural network considering the optimal parameters were verified. When the number of nodes in the input layer was five (digital number, UAV sensor height, wavelength, solar altitude angle, and temperature) and the number of nodes in the hidden layer was eight, the BP neural network had the best comprehensive performance in training time of train set and accuracy of validation/test set. In the aspect of accuracy and robustness, the absolute errors of test and cross-validation images' surface reflectance obtained by the BPNNRCM were all less than 0.054. The BPNNRCM had smaller average absolute error (0.0141), mean squared error (0.0003), mean absolute error (0.0141) and mean relative error (7.1%) comparing with empirical line method and radiative transfer model. In general, the research results of this article prove the feasibility and prospect of BPNNRCM for radiometric correction of UAV multispectral images.

An Intelligent Adequate-Fitting Prediction Method of Coastal Tunnel Rock Deformation Based on the Effective Rank of Hidden Layer

The intelligent prediction of surrounding rock deformation is of great significance for guiding the design and construction of tunnel projects in coastal areas. The deformation of tunnels in coastal areas is more complex than that of the ground, and the risk of encountering adverse geological conditions is greater. The traditional tunnel deformation prediction method contains the defects of a fixed model, a limited sample number, and it is easy to fall into underfitting and local overfitting. Therefore, the capacity of previous methods is limited by significant error, weak generalization, and poor intelligence. This paper proposes an adequate fitting prediction method for tunnel deformation based on the effective rank theory of the hidden layer nodes’ output matrix to analyze the surrounding rock and predict its deformation intelligently. Based on the traditional BPNN (back propagation neural network) algorithm, the number of hidden layer nodes is determined by the effective rank of the output matrix. Then, the approximation error and degree were adopted to reflect the approximation law of the BPNN to achieve the purpose of overfitting and underfitting control. An optimized BP neural network model for intelligently predicting tunnel deformation is constructed. Then, the optimized BPNN model is applied to a case study of a coastal tunnel in South China. Compared with the prediction method of LR (linear regression) and TS (time series), the results show that the prediction results of the optimized model are in good agreement with the measured values, with strong generalization ability and high intelligence. The proposed method is of guidance to other tunnels surrounding rock deformation prediction and engineering practice.

An improved deep extreme learning machine to predict the remaining useful life of lithium-ion battery

To aim at the problem of inaccurate prediction of the remaining useful life of the lithium-ion battery, an improved grey wolf optimizer optimizes the deep extreme learning machine (CGWO-DELM) data-driven forecasting method is proposed. This method uses the grey wolf optimization algorithm based on an adaptive normal cloud model to optimize the bias of the deep extreme learning machine, the weight of the input layer, the selection of activation function, and the number of hidden layer nodes. In this article, indirect health factors that can characterize the degradation of battery performance are extracted from the discharge process, and the correlation between them and capacity is analyzed using the Pearson coefficient and Kendel coefficient. Then, the CGWO-DELM prediction model is constructed to predict the capacitance of the lithium-ion battery. The remaining useful life of lithium-ion batteries is indirectly predicted with a 1.44 A·h failure threshold. The prediction results are compared with deep extreme learning machines, long-term memory, other prediction methods, and the current public prediction methods. The results show that the CGWO-DELM prediction method can more accurately predict the remaining useful life of lithium-ion batteries.

Study on inversion method of wall erosion information of on-orbit Hall thruster based on low-frequency oscillation signals and neural networks

Hall thrusters function as power plants on spacecraft, and its development is crucial for the aerospace industry. The wall erosion of the on-orbit Hall thruster cannot be measured by the control center through ground measurement, but can obtain the discharge current low-frequency oscillation data. Therefore, this study proposes an inversion method to obtain the wall erosion information based on low-frequency oscillation signals and neural networks. Firstly, we use an improved one-dimensional quasi-neutral dynamic fluid mathematical model to build a low-frequency oscillation simulation platform which obtains the corresponding data by varying the cross-sectional area. Secondly, a nonlinear neural network model is established based on the obtained low-frequency oscillation data to invert the wall erosion information. The training function, transfer function, number of hidden layer nodes, and other parameters affecting the results are analyzed and the best model parameters are obtained. The Elman neural network is established and compared with the BP neural network and RBF neural network. The training results of the Elman neural network algorithm present small and stable errors, and the results of multiple predictions remain consistent. The root means square error, average absolute error, and average absolute percentage are 0.0084, 0.0637, and 0.045%, respectively.

Fall Detection System Based on Simple Threshold Method and Long Short-Term Memory: Comparison with Hidden Markov Model and Extraction of Optimal Parameters

In an aging global society, a few complex problems have been occurring due to falls among the increasing elderly population. Therefore, falls are detected using a pendant-type sensor that can be worn comfortably for fall detection. The sensed data are processed by the embedded environment and classified by a long-term memory (LSTM). A fall detection system that combines a simple threshold method (STM) and LSTM, the STM-LSTM-based fall detection system, is introduced. In terms of training data accuracy, the proposed STM-LSTM-based fall detection system is compared with the previously reported STM-hidden Markov model (HMM)-based fall detection system. The training accuracy of the STM-LSTM fall detection system is 100%, while the highest training accuracy by the STM-HMM-based one is 99.5%, which is 0.5% less than the best of the STM-LSTM-based system. In addition, in the optimized LSTM fall detection system, this may be overfitted because all data are trained without separating any validation data. In order to resolve the possible overfitting issue, training and validation data are evaluated separately in 4:1, and then in terms of validation data accuracy of the STM-LSTM-based fall detection system, optimal values of the parameters in LSTM and normalization method are found as follows: best accuracy of 98.21% at no-normalization, no-sampling, 128hidden layer nodes, and regularization rate of 0.015. It is also observed that as the number of hidden layer nodes or sampling interval increases, the regularization rate at the highest value of accuracy increases. This means that overfitting can be suppressed by increasing the regularization, and thus an appropriate number of hidden layer nodes and a regularization rate must be selected to improve the fall detection efficiency.

Short‐term passenger flow prediction for rail transit based on improved particle swarm optimization algorithm

AbstractThe subjectivity of selecting training parameters is an important factor affecting the accuracy of short‐term passenger flow prediction of rail transit by long short‐term memory (LSTM) neural network. In order to improve the prediction accuracy, an improved particle swarm optimization (IPSO) algorithm is proposed to optimize the LSTM. The size of the learning factor of the particle swarm optimization (PSO) algorithm is controlled by dynamic adjustment method to improve the global optimization and convergence ability of the algorithm. The number of hidden layer nodes, learning rate and iteration times of the LSTM are optimized by IPSO. The passenger flow data of Dongjiekou station of Fuzhou Metro Line 1 are selected for verification, and the proposed model is compared with the traditional prediction model. The results show that the LSTM optimized by the improved particle swarm optimization algorithm can effectively predict the short‐term passenger flow of rail transit. Compared with the PSO‐LSTM model, the root mean square error predicted by the IPSO‐LSTM model decreases by 10.87% in the peak period and by 26% in the off‐peak period. The results can provide theoretical and technical support for the optimization of rail transit operation scheme.

Detection Method for Walnut Shell-Kernel Separation Accuracy Based on Near-Infrared Spectroscopy.

In this study, Near-infrared (NIR) spectroscopy was adopted for the collection of 1200 spectra of three types of walnut materials after breaking the shells. A detection model of the walnut shell-kernel separation accuracy was established. The preprocessing method of de-trending (DT) was adopted. A classification model based on a support vector machine (SVM) and an extreme learning machine (ELM) was established with the principal component factor as the input variable. The effect of the penalty value (C) and kernel width (g) on the SVM model was discussed. The selection criteria of the number of hidden layer nodes (L) in the ELM model were studied, and a genetic algorithm (GA) was used to optimize the input layer weight (W) and the hidden layer threshold value (B) of the ELM. The results revealed that the classification accuracy of SVM and ELM models for the shell, kernel, and chimera was 97.78% and 97.11%. The proposed method can serve as a reference for the detection of walnut shell-kernel separation accuracy.

Rapid determination of the total content of oleanolic acid and ursolic acid in Chaenomelis Fructus using near-infrared spectroscopy.

Chaenomelis Fructus is a widely used traditional Chinese medicine with a long history in China. The total content of oleanolic acid (OA) and ursolic acid (UA) is taken as an important quality marker of Chaenomelis Fructus. In this study, quantitative models for the prediction total content of OA and UA in Chaenomelis Fructus were explored based on near-infrared spectroscopy (NIRS). The content of OA and UA in each sample was determined using high-performance liquid chromatography (HPLC), and the data was used as a reference. In the partial least squares (PLS) model, both leave one out cross validation (LOOCV) of the calibration set and external validation of the validation set were used to screen spectrum preprocessing methods, and finally the multiplicative scatter correction (MSC) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLS regression, and the characteristic spectrum range was determined as 7,500–4,250 cm−1, with 14 optimal ranks. In the back propagation artificial neural network (BP-ANN) model, the scoring data of 14 ranks obtained from PLS regression analysis were taken as input variables, and the total content of OA and UA reference values were taken as output values. The number of hidden layer nodes of BP-ANN was screened by full-cross validation (Full-CV) of the calibration set and external validation of the validation set. The result shows that both PLS model and PLS-BP-ANN model have strong prediction ability. In order to evaluate and compare the performance and prediction ability of models, the total content of OA and UA in each sample of the test set were detected under the same HPLC conditions, the NIRS data of the test set were input, respectively, to the optimized PLS model and PLS-BP-ANN model. By comparing the root-mean-square error (RMSEP) and determination coefficient (R2) of the test set and ratio of performance to deviation (RPD), the PLS-BP-ANN model was found to have better performance with RMSEP of 0.59 mg·g−1, R2 of 95.10%, RPD of 4.53 and bias of 0.0387 mg·g−1. The results indicated that NIRS can be used for the rapid quality control of Chaenomelis Fructus.

EEG Signal Classification of Motor Imagery Right and Left Hand using Common Spatial Pattern and Multilayer Perceptron Back Propagation

The number of people with disabilities is increasing, so it requires bionic devices to replace human motor functions. Brain-Computer Interface (BCI) can be a tool for the bionic device to communicate with the brain. Signal brain or Electroencephalogram (EEG) signal need to classify to drive the corresponding bionic device. This research goal is to classify the imagination of the right and left-hand movements based on the EEG signal. The system design in this research consists of EEG channel selection using Finite Impulse Response (FIR) filter, feature extraction using Common Spatial Pattern (CSP), and classification using Multilayer Perceptron Back Propagation (MLP-BP). The data used a secondary dataset from BCI Competition IV (2b) with 9 research subjects. The research scenario is carried out by trying to use several variations in the number of hidden layer nodes on each EEG channel. Based on the test, the best accuracy for MLP-BP is 68.7% using 24 nodes in the alpha channel.