Research on Prediction of the Stability of Partially Stabilized Zirconia Prepared by Microwave Heating Using Levenberg Marquardt-Back Propagation Neural Network

2012 ◽  
pp. 769-778 ◽  
Author(s):  
Lijun Liu ◽  
Shenghui Guou ◽  
Dongbo Liu ◽  
Jinhui Peng ◽  
Guo Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Microwave Heating ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Stabilized Zirconia ◽  
Partially Stabilized Zirconia ◽  
Levenberg Marquardt ◽  
The Stability

Research on Predicting the Stability of Partially Stabilized Zirconia Prepared by Microwave Heating Using SVM

2010 ◽  
Vol 146-147 ◽  
pp. 510-516
Author(s):  
Sheng Hui Guo ◽  
Li Jun Liu ◽  
Dong Bo Li ◽  
Jin Hui Peng ◽  
Li Bo Zhang ◽  
...  
Keyword(s):  
Microwave Heating ◽  
Influence Factors ◽  
Theory And Practice ◽  
Future Application ◽  
Support Vector ◽  
Stabilized Zirconia ◽  
Partially Stabilized Zirconia ◽  
Typical Data ◽  
Svm Model ◽  
The Stability

The stability is a key product performance index, which can directly determine the quality of the partially stabilized zirconia (PSZ), so how to predict the stability of PSZ accurately, quickly and easily in the preparation process is very important. In this paper, a new mathematical model to predict the stability of PSZ prepared by microwave heating was proposed, based on statistical theory (SLT) and support vector machine (SVM) theory, which relates the stability of PSZ and the influence factors, such as the holding temperature, rising rate of temperature, holding time, decreasing rate of temperature and hardening temperature. Typical data collected from 58 experiments were used for the training samples and test samples. Then testing and analyzing was done. The results showed that the SVM model is reasonable and it is accurate and reliable to predict the stability of the partially stabilized zirconia prepared by microwave heating by SVM model. Besides, multiple influence factors can be comprehensively considered in the SVM model, thus a new highly effective method for predicting the stability of PSZ prepared by microwave heating is provided for future application, which is of great significance to theory and practice.

scholarly journals Optimization of LMBP high-speed railway wheel size prediction algorithm based on improved adaptive differential evolution algorithm

2019 ◽  
Vol 15 (10) ◽  
pp. 155014771988134 ◽  
Author(s):  
Yu Zhang ◽  
Jiawen Zhang ◽  
Lin Luo ◽  
Xiaorong Gao
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Back Propagation ◽  
Differential Evolution Algorithm ◽  
Back Propagation Neural Network ◽  
Set Size ◽  
Adaptive Differential Evolution ◽  
Levenberg Marquardt ◽  
Evolution Algorithm ◽  
Local Extreme

It is beneficial for maintenance department to make maintenance strategy and reduce maintenance cost to forecast the hidden danger index value. Based on the analysis of the research status of wheel-to-life prediction at home and abroad and the repair of wheel-set wear and tear, this article designs and implements an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model. Aiming at the shortcomings of back propagation neural network, it is easy to fall into local extreme value. The back propagation algorithm is improved by Levenberg–Marquardt numerical optimization algorithm. Aiming at the shortcomings of back propagation neural network algorithm for randomly initializing connection weights and thresholds to fall into local extreme value, the differential evolution algorithm is used to optimize the initial connection weights and thresholds between the layers of the neural network. In order to speed up the search of the optimal initial weights and thresholds of the differential evolution algorithm Levenberg–Marquardt back propagation neural network, the initial values are further optimized, and an adaptive differential evolution algorithm Levenberg–Marquardt back propagation wheel-set size prediction model is designed and implemented. Compared with the proposed combine adaptive differential evolution algorithm with LMBP optimization (ADE-LMBP) is effective and significantly improves the prediction accuracy.

Influence of Gene Effect on Predicting Animal Phenotype Using Back-Propagation Artificial Neural Networks

2011 ◽  
Vol 460-461 ◽  
pp. 335-340 ◽  
Author(s):  
Xue Bin Li ◽  
Xiao Ling Yu ◽  
Yun Rui Guo ◽  
Zhi Feng Xiang ◽  
Kun Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Quantitative Traits ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Simple Relation ◽  
Gene Effects ◽  
Gene Effect ◽  
Artificial Neural ◽  
The Stability ◽  
Hidden Neurons

Recently, largescale, high-density single-nucleotide polymorphism (SNP) marker information has become available. However, the simple relation was not enough for describing the relation between markers and genotype value, and the genetic diversity should be carefully monitored as genomic selection for quantitative traits as a routine technology for animal genetic improvement. In this paper, back-propagation neural network is used to simulate and predict the genotype values, and the different gene effects were used to discuss the influences on estimating the polygenic genotype value. The results showed that after phenotype value being normalized, optimization network could be established for predicting the phenotype value without fearing that the gene effect is too large. If the number of hidden neurons is large enough, the stability of back-propagation artificial neural network established for predicting phenotype value is very well. the gene effect could not affected the precise of optimum neural network for estimating the animal phenotype, the optimum neural network could be selected for predicting the phenotype values of quantitative traits controlled by genes with small or large effects.

Back-Propagation Model for Nanofiltration Process Simulation in Pesticide Wastewater Treatment

2010 ◽  
Vol 168-170 ◽  
pp. 404-407 ◽  
Author(s):  
Qing Yang ◽  
Yong Ju Hu ◽  
Liang Xue
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Process Simulation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Propagation Model ◽  
The Stability ◽  
Back Propagation Model ◽  
Good Agreement ◽  
Separation Model

This study simulated the nanofiltration (NF) process of contamination removing by back-propagation neural network (BPNN), according to the test values of DK membrane pre-treating Imidacloprid pesticide wastewater. The real time nanofiltration (NF) separation model was presented for effective controlling of DK NF separation. The research showed the simulation precision met the application demands, with the correlation coefficient between the simulation and test rejection of COD and salt over 0.99, and absoluteness error below ±4%. In order to test the prediction of this BPNN simulation model, further NF experiments were carried out. Under the same multifactor condition, the predictions for the NF process performances were found to be in good agreement with the experimental results. This BP simulation model for NF process could be used to test the stability and effectively of NF system, and support the membrane technology well.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Sign in / Sign up

Export Citation Format

Share Document