Annual Runoff Forecast Based on Cooperative Particle Swarm Projection Pursuit Regression Model

2019 ◽  
Author(s):  
Xinxin Li ◽  
Jing Xu
Keyword(s):  
Regression Model ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Annual Runoff ◽  
Projection Pursuit Regression ◽  
Runoff Forecast

A HYBRID SUPPORT VECTOR REGRESSION APPROACH FOR RAINFALL FORECASTING USING PARTICLE SWARM OPTIMIZATION AND PROJECTION PURSUIT TECHNOLOGY

2010 ◽  
Vol 09 (02) ◽  
pp. 87-104 ◽  
Author(s):  
JIANSHENG WU ◽  
MINGZHE LIU ◽  
LONG JIN
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Support Vector Regression ◽  
Projection Pursuit ◽  
Particle Swarm ◽  
Support Vector ◽  
Rainfall Forecasting ◽  
Support Vector Regression Model ◽  
Swarm Optimization

In this paper, a hybrid rainfall-forecasting approach is proposed which is based on support vector regression, particle swarm optimization and projection pursuit technology. The projection pursuit technology is used to reduce dimensions of parameter spaces in rainfall forecasting. The particle swarm optimization algorithm is for searching the parameters for support vector regression model and to construct the support vector regression model. The observed data of daily rainfall values in Guangxi (China) is used as a case study for the proposed model. The computing results show that the present model yields better forecasting performance in this case study, compared to other rainfall-forecasting models. Our model may provide a promising alternative for forecasting rainfall application.

Study on Reliability of Hydraulic Brake System Based on Projection Pursuit Regression

2012 ◽  
Vol 198-199 ◽  
pp. 966-969
Author(s):  
Yu Cai Dong ◽  
Ge Hua Fan ◽  
Liang Hai Yi ◽  
Ling Zhang
Keyword(s):  
Regression Model ◽  
Projection Pursuit ◽  
Training Sample ◽  
Scientific Basis ◽  
Brake System ◽  
Projection Pursuit Regression ◽  
Hydraulic Brake ◽  
Regression Theory

The projection pursuit regression theory is applied to analysis the reliability of vehicle hydraulic brake system and build the projection pursuit regression model. This model on training sample fitting effect is good and shows extremely strong adaptability. We predict the reliability of certain type hydraulic brake system by this model which provides scientific basis for research on reliability of hydraulic brake system.

Application of Projection Pursuit Regression in Equipment Indemnificatory Valuation and Forecast

2013 ◽  
Vol 411-414 ◽  
pp. 2111-2114
Author(s):  
Lian Jun Zhu ◽  
Hong Yan Li ◽  
Yu Cai Dong ◽  
Tian Yuan Jiang ◽  
Ge Hua Fan
Keyword(s):  
Regression Model ◽  
Relative Error ◽  
Projection Pursuit ◽  
Influential Factors ◽  
Average Relative Error ◽  
Projection Pursuit Regression ◽  
Good Balance ◽  
Training Samples ◽  
Good Forecast

The Theory of Projection Pursuit Regression is applied in the equipment indemnificatory valuation and forecast to establish the projection pursuit regression model. After fitting the training samples, this model strikes a good balance between the valuation value and its relevant influential factors, demonstrating a good fitting effect with the average relative error of only 2.1522% . After predicting the test samples, it shows a good forecast effect with the relative error of only-0.4069%, thus providing basis for equipment indemnificatory valuation and forecast.

scholarly journals Tree-Structured Regression Model Using a Projection Pursuit Approach

2021 ◽  
Vol 11 (21) ◽  
pp. 9885
Author(s):  
Hyunsun Cho ◽  
Eun-Kyung Lee
Keyword(s):  
Random Forest ◽  
Regression Model ◽  
Classification Tree ◽  
Projection Pursuit ◽  
Regression Tree ◽  
Projection Pursuit Regression ◽  
Single Tree ◽  
Variable Space ◽  
Independent Variable ◽  
Structured Regression

In this paper, we propose a new tree-structured regression modelthe projection pursuit regression tree.a new tree-structured regression model—the projection pursuit regression tree—is proposed. It combines the projection pursuit classification tree with the projection pursuit regression. The main advantage of the projection pursuit regression tree is exploring the independent variable space in each range of the dependent variable. Additionally, it retains the main properties of the projection pursuit classification tree. The projection pursuit regression tree provides several methods of assigning values to the final node, which enhances predictability. It shows better performance than CART in most cases and sometimes beats random forest with a single tree. This development makes it possible to find a better explainable model with reasonable predictability.

Statistical process monitoring for e-waste based on beta regression and particle swarm optimization

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Angelo Marcio Oliveira Sant’Anna
Keyword(s):  
Particle Swarm Optimization ◽  
Regression Model ◽  
Process Monitoring ◽  
Manufacturing Process ◽  
Particle Swarm ◽  
Injection Rate ◽  
Statistical Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Statistical Process Monitoring

PurposeE-waste management can reduce relevant impact of the business activity without affecting reliability, quality or performance. Statistical process monitoring is an effective way for managing reliability and quality to devices in manufacturing processes. This paper proposes an approach for monitoring the proportion of e-waste devices based on Beta regression model and particle swarm optimization. A statistical process monitoring scheme integrating residual useful life techniques for efficient monitoring of e-waste components or equipment was developed.Design/methodology/approachAn approach integrating regression method and particle swarm optimization algorithm was developed for increasing the accuracy of regression model estimates. The control chart tools were used for monitoring the proportion of e-waste devices from fault detection of electronic devices in manufacturing process.FindingsThe results showed that the proposed statistical process monitoring was an excellent reliability and quality scheme for monitoring the proportion of e-waste devices in toner manufacturing process. The optimized regression model estimates showed a significant influence of the process variables for both individually injection rate and toner treads and the interactions between injection rate, toner treads, viscosity and density.Originality/valueThis research is different from others by providing an approach for modeling and monitoring the proportion of e-waste devices. Statistical process monitoring can be used to monitor waste product in manufacturing. Besides, the key contribution in this study is to develop different models for fault detection and identify any change point in the manufacturing process. The optimized model used can be replicated to other Electronic Industry and allows support of a satisfactory e-waste management.

scholarly journals Impact of measurement errors on the inferred stellar asteroseismic ages

2018 ◽  
Vol 620 ◽  
pp. A168 ◽  
Author(s):  
G. Valle ◽  
M. Dell’Omodarme ◽  
P. G. Prada Moroni ◽  
S. Degl’Innocenti
Keyword(s):  
Effective Temperature ◽  
Measurement Errors ◽  
Regression Models ◽  
Linear Models ◽  
Projection Pursuit ◽  
Observational Constraints ◽  
Error Source ◽  
Projection Pursuit Regression ◽  
The Impact ◽  
Age Estimates

Aims. We aim to perform a theoretical investigation on the direct impact of measurement errors in the observational constraints on the recovered age for stars in main sequence (MS) and red giant branch (RGB) phases. We assumed that a mix of classical (effective temperature Teff and metallicity [Fe/H]) and asteroseismic (Δν and νmax) constraints were available for the objects. Methods. Artificial stars were sampled from a reference isochrone and subjected to random Gaussian perturbation in their observational constraints to simulate observational errors. The ages of these synthetic objects were then recovered by means of a Monte Carlo Markov chains approach over a grid of pre-computed stellar models. To account for observational uncertainties the grid covers different values of initial helium abundance and mixing-length parameter, that act as nuisance parameters in the age estimation. Results. The obtained differences between the recovered and true ages were modelled against the errors in the observables. This procedure was performed by means of linear models and projection pursuit regression models. The first class of statistical models provides an easily generalizable result, whose robustness is checked with the second method. From linear models we find that no age error source dominates in all the evolutionary phases. Assuming typical observational uncertainties, for MS the most important error source in the reconstructed age is the effective temperature of the star. An offset of 75 K accounts for an underestimation of the stellar age from 0.4 to 0.6 Gyr for initial and terminal MS. An error of 2.5% in νmax resulted the second most important source of uncertainty accounting for about −0.3 Gyr. The 0.1 dex error in [Fe/H] resulted particularly important only at the end of the MS, producing an age error of −0.4 Gyr. For the RGB phase the dominant source of uncertainty is νmax, causing an underestimation of about 0.6 Gyr; the offset in the effective temperature and Δν caused respectively an underestimation and overestimation of 0.3 Gyr. We find that the inference from the linear model is a good proxy for that from projection pursuit regression models. Therefore, inference from linear models can be safely used thanks to its broader generalizability. Finally, we explored the impact on age estimates of adding the luminosity to the previously discussed observational constraints. To this purpose, we assumed – for computational reasons – a 2.5% error in luminosity, much lower than the average error in the Gaia DR2 catalogue. However, even in this optimistic case, the addition of the luminosity does not increase precision of age estimates. Moreover, the luminosity resulted as a major contributor to the variability in the estimated ages, accounting for an error of about −0.3 Gyr in the explored evolutionary phases.

scholarly journals Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression

2016 ◽  
Vol 20 (12) ◽  
pp. 4717-4729 ◽  
Author(s):  
Martin Durocher ◽  
Fateh Chebana ◽  
Taha B. M. J. Ouarda
Keyword(s):  
Target Location ◽  
Projection Pursuit ◽  
Flood Frequency ◽  
Flood Frequency Analysis ◽  
Projection Pursuit Regression ◽  
Index Flood ◽  
Regional Flood Frequency Analysis ◽  
Quantile Estimates ◽  
Target Locations ◽  
Hydrological Variables

Abstract. This study investigates the utilization of hydrological information in regional flood frequency analysis (RFFA) to enforce desired properties for a group of gauged stations. Neighbourhoods are particular types of regions that are centred on target locations. A challenge for using neighbourhoods in RFFA is that hydrological information is not available at target locations and cannot be completely replaced by the available physiographical information. Instead of using the available physiographic characteristics to define the centre of a target location, this study proposes to introduce estimates of reference hydrological variables to ensure a better homogeneity. These reference variables represent nonlinear relations with the site characteristics obtained by projection pursuit regression, a nonparametric regression method. The resulting neighbourhoods are investigated in combination with commonly used regional models: the index-flood model and regression-based models. The complete approach is illustrated in a real-world case study with gauged sites from the southern part of the province of Québec, Canada, and is compared with the traditional approaches such as region of influence and canonical correlation analysis. The evaluation focuses on the neighbourhood properties as well as prediction performances, with special attention devoted to problematic stations. Results show clear improvements in neighbourhood definitions and quantile estimates.

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