scholarly journals Seepage Time Soft Sensor Model of Nonwoven Fabric Based on the Extreme Learning Machine Integrating Monte Carlo

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2377
Author(s):  
Jing Zhang ◽  
Yiqiang Fan ◽  
Lulu Zhang ◽  
Chi Xu ◽  
Xiaobin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Prediction Model ◽  
Structural Characteristics ◽  
Nonwoven Fabric ◽  
Soft Sensor ◽  
Sensor Model ◽  
Learning Machine ◽  
Fiber Materials ◽  
Nonwoven Fiber

Nonwoven fiber materials are materials with multifunctional purposes, and are widely used to make masks for preventing the new Coronavirus Disease 2019. Because of the complexity and particularity of their structure, it becomes difficult to model the penetration and flow characteristics of liquid in nonwoven fiber materials. In this paper, a novel seepage time soft sensor model of nonwoven fabric, based on Monte Carlo (MC), integrating extreme learning machine (ELM) (MCELM) is proposed. The Monte Carlo method is used to expand data samples. Then, an ELM method is used to establish the prediction model of the dyeing time of the nonwoven fiber material overlaps with the porous medium, as well as the insertion degree and height of the different quantity of hides. Compared with the back propagation (BP) neural network and radial basis function (RBF) neural network, the results show that the prediction model based on the MCELM method has significant power in terms of accuracy and prediction speed, which is conducive to the precise and rapid manufacture of nonwoven fiber materials in practical applications between liquid seepage characteristics and structural characteristics of porous media. Furthermore, the relationship between the proposed models has certain value for predicting the behavior and use of nonwoven fiber materials with different structural characteristics and related research processes.

Soft sensor model for dynamic processes based on multichannel convolutional neural network

2020 ◽  
Vol 203 ◽  
pp. 104050 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Shuaibin Qi ◽  
Yuri A.W. Shardt ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Soft Sensor ◽  
Dynamic Processes ◽  
Sensor Model

scholarly journals Hybrid Multiple Soft-Sensor Models of Grinding Granularity Based on Cuckoo Searching Algorithm and Hysteresis Switching Strategy

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jie-Sheng Wang ◽  
Na-Na Shen
Keyword(s):  
Neural Network ◽  
Soft Sensor ◽  
Grinding Process ◽  
Real Time Control ◽  
Switching Strategy ◽  
Time Control ◽  
Sensor Model ◽  
Optimum Model ◽  
Set Up ◽  
Sampling Instant

According to the characteristics of grinding process and accuracy requirements of technical indicators, a hybrid multiple soft-sensor modeling method of grinding granularity is proposed based on cuckoo searching (CS) algorithm and hysteresis switching (HS) strategy. Firstly, a mechanism soft-sensor model of grinding granularity is deduced based on the technique characteristics and a lot of experimental data of grinding process. Meanwhile, the BP neural network soft-sensor model and wavelet neural network (WNN) soft-sensor model are set up. Then, the hybrid multiple soft-sensor model based on the hysteresis switching strategy is realized. That is to say, the optimum model is selected as the current predictive model according to the switching performance index at each sampling instant. Finally the cuckoo searching algorithm is adopted to optimize the performance parameters of hysteresis switching strategy. Simulation results show that the proposed model has better generalization results and prediction precision, which can satisfy the real-time control requirements of grinding classification process.

scholarly journals Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Jie-sheng Wang ◽  
Shuang Han ◽  
Na-na Shen ◽  
Shu-xia Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Soft Sensor ◽  
Features Extraction ◽  
Image Processing Technique ◽  
Flotation Process ◽  
Sensor Model

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.

scholarly journals A Hybrid Model for Monthly Precipitation Time Series Forecasting Based on Variational Mode Decomposition with Extreme Learning Machine

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 177 ◽  
Author(s):  
Guohui Li ◽  
Xiao Ma ◽  
Hong Yang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Hybrid Model ◽  
Prediction Accuracy ◽  
Monthly Precipitation ◽  
Variational Mode Decomposition ◽  
Precipitation Time Series ◽  
Mode Decomposition ◽  
Learning Machine

The matter of success in forecasting precipitation is of great significance to flood control and drought relief, and water resources planning and management. For the nonlinear problem in forecasting precipitation time series, a hybrid prediction model based on variational mode decomposition (VMD) coupled with extreme learning machine (ELM) is proposed to reduce the difficulty in modeling monthly precipitation forecasting and improve the prediction accuracy. The monthly precipitation data in the past 60 years from Yan’an City and Huashan Mountain, Shaanxi Province, are used as cases to test this new hybrid model. First, the nonstationary monthly precipitation time series are decomposed into several relatively stable intrinsic mode functions (IMFs) by using VMD. Then, an ELM prediction model is established for each IMF. Next, the predicted values of these components are accumulated to obtain the final prediction results. Finally, three predictive indicators are adopted to measure the prediction accuracy of the proposed hybrid model, back propagation (BP) neural network, Elman neural network (Elman), ELM, and EMD-ELM models: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). The experimental simulation results show that the proposed hybrid model has higher prediction accuracy and can be used to predict the monthly precipitation time series.

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