Projection Pursuit Regression Based on Hermite Polynomial for Atmospheric Corrosion Data Application

2014 ◽  
Vol 881-883 ◽  
pp. 1747-1753
Author(s):  
Wei Dong Nie ◽  
Xiao Ming Wang ◽  
Zhao Na Li ◽  
Xin Geng Li
Keyword(s):  
Atmospheric Corrosion ◽  
Hermite Polynomial ◽  
Influence Factors ◽  
Projection Pursuit ◽  
Steel Corrosion ◽  
Regression Method ◽  
Small Sample ◽  
Projection Pursuit Regression ◽  
Data Application ◽  
Corrosion Data

A Projection Pursuit Regression method by using Hermite Polynomial is put forward to make modeling and forcasting of corrosion data, because of small sample of acumulation data of metal material corrosion in atmosphere, Multi-dimensional Properties and Non-orthogonality of influence factors. Analyses and prediction of atmospheric corrosion data of a metal are made by using this method. Compared with PCA+SVM method, this method improves significantly the accuration of prediction and correctness of corrosion vehavior trend. The result proves that the Hermite Polynomial Projection Pursuit Regression method has great huge advantage in data analysis of steel corrosion in atmosphere.

scholarly journals Establishing grassland yield models using Projection Pursuit Regression Method

2005 ◽  
Vol 48 (1) ◽  
pp. 47-55
Author(s):  
Li Jianlong ◽  
QI Jiaguo ◽  
Zhao Dehua ◽  
Jiang Ping ◽  
Xu Sheng
Keyword(s):  
Projection Pursuit ◽  
Regression Method ◽  
Projection Pursuit Regression

Application of Projection Pursuit Regression in the Fault Diagnose of Advanced Amphibious Assault Vehicle Hydraulic Motor Based on Applied Mechanics

2012 ◽  
Vol 485 ◽  
pp. 310-313
Author(s):  
Yu Cai Dong ◽  
Ge Hua Fan ◽  
Liang Hai Yi ◽  
Ling Zhang ◽  
Min Lin
Keyword(s):  
Hydraulic System ◽  
Projection Pursuit ◽  
Regression Method ◽  
Influential Factor ◽  
Applied Mechanics ◽  
Fault Diagnose ◽  
Hydraulic Motor ◽  
High Failure Rate ◽  
Projection Pursuit Regression ◽  
The Relationship

The hydraulic motor of amphibious assault vehicle is one of the important output executive components of the hydraulic system whose performance has an important influence for the whole system. According to the high failure rate, fault detection and location problems of hydraulic system, this paper explore the relationship between the leakage of amphibious assault vehicle and each influential factor, establish the leakage predication model by projection pursuit regression method and get a better predicated effect. It has great significance for the fault diagnosis of hydraulic motor of amphibious assault vehicle.

Application of the Projection Pursuit Regression in Forecasting the Torpedo Velocity at Tube Outlet

2014 ◽  
Vol 551 ◽  
pp. 365-369
Author(s):  
Jia Xing Du ◽  
Ping Chen ◽  
Ling Zhang ◽  
Juan Min Xiang ◽  
Hui Zhen Li
Keyword(s):  
Model Prediction ◽  
Projection Pursuit ◽  
Regression Method ◽  
Cylinder Pressure ◽  
Experiment Data ◽  
Projection Pursuit Regression ◽  
Valve Size ◽  
Relation Model ◽  
Ballistic Trajectory ◽  
Fitting Accuracy

Through the relations among the torpedo velocity at tube outlet, cylinder volume, cylinder pressure and emission valve size, we adopt the projection pursuit regression method and establish the model to predict the torpedo velocity at tube outlet. Through the comparison of experiment data and model prediction, fitting accuracy of this relation model is stronger and the projection pursuit regression is an efficient method which can research the initial ballistic trajectory of ship-launched torpedoes.

scholarly journals Data Mining to Atmospheric Corrosion Process Based on Evidence Fusion

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6954
Author(s):  
Jintao Meng ◽  
Hao Zhang ◽  
Xue Wang ◽  
Yue Zhao
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Atmospheric Corrosion ◽  
Correlation Coefficients ◽  
Steel Corrosion ◽  
Underlying Mechanism ◽  
Research Field ◽  
Support Vector ◽  
Time Data ◽  
Corrosion Data

An electrical resistance sensor-based atmospheric corrosion monitor was employed to study the carbon steel corrosion in outdoor atmospheric environments by recording dynamic corrosion data in real-time. Data mining of collected data contributes to uncovering the underlying mechanism of atmospheric corrosion. In this study, it was found that most statistical correlation coefficients do not adapt to outdoor coupled corrosion data. In order to deal with online coupled data, a new machine learning model is proposed from the viewpoint of information fusion. It aims to quantify the contribution of different environmental factors to atmospheric corrosion in different exposure periods. Compared to the commonly used machine learning models of artificial neural networks and support vector machines in the corrosion research field, the experimental results demonstrated the efficiency and superiority of the proposed model on online corrosion data in terms of measuring the importance of atmospheric factors and corrosion prediction accuracy.

Combining Exploratory Projection Pursuit and Projection Pursuit Regression with Application to Neural Networks

1993 ◽  
Vol 5 (3) ◽  
pp. 443-455 ◽  
Author(s):  
Nathan Intrator
Keyword(s):  
Neural Networks ◽  
Projection Pursuit ◽  
Regression Method ◽  
Projection Pursuit Regression ◽  
Supervised Training ◽  
New Family ◽  
Unsupervised Training ◽  
Classification And Regression ◽  
Exploratory Projection Pursuit ◽  
Real World Problems

We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real-world problems.

Modeling research of initial atmospheric corrosion of Q235 carbon steel based on electrical resistance probe

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhuolin Li ◽  
Dongmei Fu ◽  
Zibo Pei
Keyword(s):  
Carbon Steel ◽  
Corrosion Rate ◽  
Real Time ◽  
Electrical Resistance ◽  
Atmospheric Corrosion ◽  
Steel Corrosion ◽  
Classification Model ◽  
Content Type ◽  
Q235 Carbon Steel ◽  
Corrosion Data

Purpose This paper aims to discover the mathematical model for Q235 carbon steel corrosion date acquired in the initial stage of atmospheric corrosion using electrical resistance probe. Design/methodology/approach In this paper, mathematical approaches are used to construct a classification model for atmospheric environmental elements and material corrosion rates. Findings Results of the experiment show that the corrosion data can be converted into corrosion depth for calculating corrosion rate to obtain corrosion kinetics model and conform corrosion acceleration phase. Combined with corresponding atmospheric environmental elements, a real time grade subdivision model for corrosion rate can be constructed. Originality/value These mathematical models constructed by real time corrosion data can be well used to research the characteristics about initial atmospheric corrosion of Q235 carbon steel.

Application of the Projection Pursuit Regression in Forecasting the Pressure of the Torpedo Launch Tube

2014 ◽  
Vol 533 ◽  
pp. 44-47
Author(s):  
Ling Zhang ◽  
Yu Cai Dong ◽  
Bao Hong Lu ◽  
Ge Hua Fan
Keyword(s):  
Projection Pursuit ◽  
Regression Method ◽  
Cylinder Pressure ◽  
Least Squares Regression ◽  
Experiment Data ◽  
Projection Pursuit Regression ◽  
Valve Size ◽  
Relation Model ◽  
Ballistic Trajectory ◽  
Launch Tube

Through the relation among the pressure of the torpedo launch tube, cylinder volume, cylinder pressure and emission valve size, we adopt the projection pursuit regression method and establish the model to predict the pressure of the torpedo launch tube. Through the comparison of experiment data and model prediction, fitting accuracy of this relation model is higher than that of the partial least-squares regression method and linear regression model and the projection pursuit regression is an efficient method which can research the initial ballistic trajectory of ship launched torpedoes.

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