Relevant bibliographies by topics / Multi-parameter regression (MPR)

Academic literature on the topic 'Multi-parameter regression (MPR)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Multi-parameter regression (MPR)"

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Moskal, Michał, Piotr Migas, and Mirosław Karbowniczek. "Multi-Parameter Characteristics of Electric Arc Furnace Melting." Materials 15, no. 4 (February 21, 2022): 1601. http://dx.doi.org/10.3390/ma15041601.

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The article presents the results of analyses of numerical modelling of selected factors in electric arc furnace melts. The aim of the study was to optimise the melting process in an electric arc furnace using statistical-thermodynamic modelling based on, among other things, multiple linear regression (MLR). The article presents tools and methods which make it possible to identify the most significant indicators of the process carried out on the analysed unit from the point of view of improvement. The article presents the characteristics of the process and creation of the MLR model and, by applying its numerical analyses and results of calculations and simulations for selected variables and indicator, identifying the operation of a selected furnace. Developed model to demand of electric energy identification was used for calculations of energy balances, the distribution of the energy used in the furnace was presented.
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Tomar, Prashant Singh. "Rainfall Simulation using ANN based Multilayer Perceptron (MLP) and Multiple Linear Regression (MLR) Technique for Bhopal, Madhya Pradesh." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 15, 2021): 1069–76. http://dx.doi.org/10.22214/ijraset.2021.36470.

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Rainfall forecasting represents a tremendously significant matter in field of hydrology. In this study, was undertaken to develop and evaluate the applicability of Multilayer Perceptron (MLP) and Multi Linear Regression (MLR) techniques. The performance of the developed models, on the basis of training and testing, was judged on the basis of four statistical measures such as Root Mean Squared Error (MSE), Coefficient of Efficiency (CE), Correlation Coefficient (r) and Coefficient of Determination(R2) during monsoon period (June to September) for Bhopal, Madhya Pradesh, India. The daily data of minimum temperature, maximum temperature, wind speed and relative humidity were used for rainfall prediction. The appropriate parameter combination of input variables for MLP was used to predict rainfall. The Neuro Solution 5.0 software and Microsoft Excel were used in analysis and the performance evaluation of developed models, respectively. The input pairs in the training data set were applied to the network of a selected architecture and training was performed using back propagation algorithm for MLP models was designed with Gaussian membership function, Takagi- Sugeno- Kang fuzzy model, hyperbolic tangent activation function and Delta-Bar-Delta learning algorithm. Ten MLP models and MLR were selected based on the performance evaluation indices during testing period. MLP models were found to be much closer to the observed values of rainfall as compared to MLR.
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Cai, C. Z., X. J. Zhu, J. F. Pei, and G. L. Wang. "Study on the Process Optimization of Synthesizing Co3O4 Nanoparticles by Homogeneous Precipitation Based on Support Vector Regression." Materials Science Forum 689 (June 2011): 211–19. http://dx.doi.org/10.4028/www.scientific.net/msf.689.211.

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The Co3O4 is the major raw material for fabricating lithium cobalt oxide electrode of lithium ion battery. According to the experimental dataset on grain diameter of Co3O4 nanoparticles synthesized by homogeneous precipitation under four main process parameters including the concentration of Co(NO3)2•6H2O solution, mole ratio of reactants, reaction temperature and reaction time, support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, is introduced to establish a model for estimating grain diameter of Co3O4 nanoparticles. The comparison of prediction results strongly support the prediction and generalization abilities of SVR are superior to those of multivariable gradual regression (MGR). Meanwhile, the index of grain diameter of Co3O4 nanoparticles under an independent combination of process parameters predicted by SVR model is more accurate than that by MGR model. The multi-factors analysis results based on SVR model are consistent with that of the literatures. This study suggests that SVR is a theoretical significance and potential practical value in development of smaller grain diameter of Co3O4 nanoparticles via guiding experiment.
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Yasmirullah, S. D. P., B. W. Otok, J. D. T. Purnomo, and D. D. Prastyo. "Parameter Estimation of Multivariate Adaptive Regression Spline (MARS) with Stepwise Approach to Multi Drug-Resistant Tuberculosis (MDR-TB) Modeling in Lamongan Regency." Journal of Physics: Conference Series 1752, no. 1 (February 1, 2021): 012017. http://dx.doi.org/10.1088/1742-6596/1752/1/012017.

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Zhou, Wengang. "Aero-engine exhaust gas temperature prediction based on adaptive disturbance quantum-behaved particle swarm optimization." Advances in Mechanical Engineering 14, no. 8 (August 2022): 168781322211190. http://dx.doi.org/10.1177/16878132221119044.

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Aircraft engine EGT (exhaust gas temperature) is uncertain. In order to reduce the EGT influence to the health of the engine, it is important to carry out the prediction. A novel EGT prediction method based on the combination method is proposed. Firstly, MIV (Mean Impact Value) was used to reduce the dimension of the input numbers. Second, the EGT was predicted by some single models such as BP (back propagation) neural network model, SVR (support vector regression) model, PLS (partial least square) model, GM(1,N) (multi-parameter input gray prediction model), MLR (multiple linear regression) model. Then absolute mean error was used to evaluate the predictive results of single models and the best predictive results of four single methods were selected to establish combination model with PSO (particle swarm optimization). Finally, the combination model was used in predicting the EGT of V2500 aero-engine. Experiment results show that the combination method is more reliable and suitable than the single models for aero-engine EGT prediction.
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Candra Damis Widiawaty, Ahmad Indra Siswantara, Gun Gun R Gunadi, Mohamad Arif Andira, Budiarso, Muhammad Arif Budiyanto, M. Hilman Gumelar Syafei, and Dendy Adanta. "Optimization of inverse-Prandtl of Dissipation in standard k-ε Turbulence Model for Predicting Flow Field of Crossflow Turbine." CFD Letters 14, no. 1 (January 11, 2022): 112–27. http://dx.doi.org/10.37934/cfdl.14.1.112127.

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Despite the successful use of the Standard model in simulating turbulent flow for many industrially relevant flows, the model is still less accurate for a range of important problems, such as unconfined flows, curved boundary layers, rotating flows, and recirculating flows. As part of the authors’ effort to extend the model applicability and reliability, this paper aims to study the effects of diffusivity parameter called the turbulent Prandtl number of dissipation rate () on the Standard model performance for predicting recirculating flow in a crossflow water turbine. The value of this parameter was varied from 0.5 to 1.5 in the CFD simulations, and the results were compared to the more sophisticated model, namely the RNG , which has been first qualitatively validated by an experimental result. In addition, the parameter value was also adjusted using the Multi-Linear Regression (MLR) method ranging from 0.42 to 1.5 to complement the CFD simulations. It was observed that reducing the value is effective in minimizing the average deviation of the turbulence properties concerning the RNG model. However, the adjusted model still faces difficulty in accurately predicting the pressure and velocity field. Based on this result, adjusting the constant in the Standard turbulence model has the potential to improve the model performance for modelling recirculating flow in terms of the turbulence properties, but still needs further investigation for the flow properties.
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Eric, Slavica, Marko Kalinic, Aleksandar Popovic, Halid Makic, Elvisa Civic, and Merja Bektasevic. "The importance of the accuracy of the experimental data for the prediction of solubility." Journal of the Serbian Chemical Society 75, no. 4 (2010): 483–95. http://dx.doi.org/10.2298/jsc090809022e.

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Aqueous solubility is an important factor influencing several aspects of the pharmacokinetic profile of a drug. Numerous publications present different methodologies for the development of reliable computational models for the prediction of solubility from structure. The quality of such models can be significantly affected by the accuracy of the employed experimental solubility data. In this work, the importance of the accuracy of the experimental solubility data used for model training was investigated. Three data sets were used as training sets - Data Set 1 containing solubility data collected from various literature sources using a few criteria (n = 319), Data Set 2 created by substituting 28 values from Data set 1 with uniformly determined experimental data from one laboratory (n = 319) and Data Set 3 created by including 56 additional components, for which the solubility was also determined under uniform conditions in the same laboratory, in the Data Set 2 (n = 375). The selection of the most significant descriptors was performed by the heuristic method, using one-parameter and multi-parameter analysis. The correlations between the most significant descriptors and solubility were established using multi-linear regression analysis (MLR) for all three investigated data sets. Notable differences were observed between the equations corresponding to different data sets, suggesting that models updated with new experimental data need to be additionally optimized. It was successfully shown that the inclusion of uniform experimental data consistently leads to an improvement in the correlation coefficients. These findings contribute to an emerging consensus that improving the reliability of solubility prediction requires the inclusion of many diverse compounds for which solubility was measured under standardized conditions in the data set.
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Pandey, S., N. R. Patel, A. Danodia, and R. Singh. "DISCRIMINATION OF SUGARCANE CROP AND CANE YIELD ESTIMATION USING LANDSAT AND IRS RESOURCESAT SATELLITE DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 229–33. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-229-2019.

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<p><strong>Abstract.</strong> The objective of this research work aims at crop acreage estimation at mill catchment level, derivation of sugarcane phenology and yield estimation at field level. The study was carried out in Kisan Sahkari Chini Mill catchment, Nanauta, Saharanpur, Uttar Pradesh. Extensive and systematic field sampling was carried out for ground-truth observations, biophysical measurements (LAI and above/below canopy PAR) and mill-able cane yield through crop cutting experiments. Major emphasis were laid on sugarcane crop discrimination, biophysical parameter estimation, generation of phenological metrics and yield model development for sugarcane crop at mill catchment level. Sugarcane crop discrimination and its acreage estimation was done using multi-sensor satellite data. The sugarcane classification accuracies were &amp;gt;&amp;thinsp;92% for LISS-IV, &amp;gt;&amp;thinsp;86% for Landsat-8 and &amp;gt;&amp;thinsp;83% for LISS-III classified image. The sugarcane phenological matrices at field level derived using time-series of NDVI for a period of 2015&amp;ndash;2016 through TIMESAT software. To retrieve the biophysical parameters particularly leaf area index, best predictive function developed with vegetation indices (EVI, NDVI, SAVI) through correlation and regression analysis along this cane yield estimation attempted with multi-date (eight-day) NDVI from Landsat OLI. Yield models developed for ratoon cane and planted cane explained variance in yield significantly with coefficient of determination (R<sup>2</sup>) values equal to 0.83 and 0.69, respectively. Similar predictive functions were also established with monthly composite dataset for village-level yield estimates with step wise regression (R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.83) (P&amp;thinsp;=&amp;thinsp;0.00001), Multi linear regression (MLR) (R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.792) (P&amp;thinsp;=&amp;thinsp;0.00081) and Random forest regression (R<sup>2</sup>&amp;thinsp;=&amp;thinsp;0.466) (P&amp;thinsp;=&amp;thinsp;0.038).</p>
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Zhu, Hongxin, Kun Zou, and Wenlan Bao. "Study on process of chemical fiber filament automatic doffing system based on simulation platform and machine learning." Journal of Engineered Fibers and Fabrics 16 (January 2021): 155892502110548. http://dx.doi.org/10.1177/15589250211054833.

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In recent years, a large number of automatic equipment has been introduced into the chemical fiber filament doffing production line, but the related research on the fully automatic production line technology is not yet mature. At present, it is difficult to collect data due to test costs and confidentiality. This paper proposes to develop a simulation platform for a chemical fiber filament doffing production line, which enables us to effectively obtain data and quantitatively study the relationship between the number of manual interventions and other process parameters of the production line. Considering that the parameter research is a multi-factor problem, an orthogonal test was designed by using SPSS software and was carried out by using a simulation platform. The multiple linear regression (MLR) and the neural network optimized by genetic algorithm were adopted to fit the relationship between the number of manual interventions and other parameters of the production line. The SPSS software was applied to obtain the standardized coefficients of the multiple linear regression fitting and the neural network mean impact value (MIV) algorithm was applied to obtain the magnitude and direction of the impact of different parameters on the number of manual interventions. The above results provide important reference for the design of similar new production lines and for the improvement of old production lines.
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Park, Donggeun, and Jeung Sang Go. "Design of Cyclone Separator Critical Diameter Model Based on Machine Learning and CFD." Processes 8, no. 11 (November 23, 2020): 1521. http://dx.doi.org/10.3390/pr8111521.

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In this paper, the characteristics of the cyclone separator was analyzed from the Lagrangian perspective for designing the important dependent variables. The neural network network model was developed for predicting the separation performance parameter. Further, the predictive performances were compared between the traditional surrogate model and the developed neural network model. In order to design the important parameters of the cyclone separator based on the particle separation theory, the force acting until the particles are separated was calculated using the Lagrangian-based computational fluid dynamics (CFD) methodology. As a result, it was proved that the centrifugal force and drag acting on the critical diameter having a separation efficiency of 50% were similar, and the particle separation phenomenon in the cyclone occurred from the critical diameter, and it was set as an important dependent variable. For developing a critical diameter prediction model based on machine learning and multiple regression methods, unsteady-Reynolds averaged Navier-Stokes analyzes according to shape dimensions were performed. The input design variables for predicting the critical diameter were selected as four geometry parameters that affect the turbulent flow inside the cyclone. As a result of comparing the model prediction performances, the machine learning (ML) model, which takes into account the critical diameter and the nonlinear relationship of cyclone design variables, showed a 32.5% improvement in R-square compared to multi linear regression (MLR). The proposed techniques have proven to be fast and practical tools for cyclone design.
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Book chapters on the topic "Multi-parameter regression (MPR)"

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Banik, Anirban, Mrinmoy Majumder, Sushant Kumar Biswal, and Tarun Kanti Bandyopadhyay. "Development of Self-Organized Group Method of Data Handling (GMDH) Algorithm to Increase Permeate Flux (%) of Helical-Shaped Membrane." In Advances in Computer and Electrical Engineering, 170–82. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3970-5.ch009.

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The chapter focuses on enhancing the permeate flux of helical shaped membrane using group method of data handling (GMDH) algorithm. The variables such as operating pressure, pore size, and feed velocity were selected as input parameters, and permeate flux as model output. The uncertainty analysis evaluates the acceptability of the model, and it was found that values of Nash-Sutcliffe efficiency (NSE), the ratio of the root mean squared error to the standard deviation (RSR), percent bias (PBIAS) were close to the best value which shows the model acceptability. The effect of input parameters on model output is calibrated using sensitivity analysis. It shows that pore size is the most sensitive parameter followed by feed velocity. The optimum values of pore size, operating pressure, and feed velocity were calibrated and found to be 2.21µm, 1.31×10-03KPa, and 0.37m/sec, respectively. The errors in GMDH model were compared with multi linear regression (MLR) model. It shows that GMDH predicts results with minimum error. The predicted variable follows the actual variables with good accuracy.
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Conference papers on the topic "Multi-parameter regression (MPR)"

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Bhardwaj, Sachin, R. M. Chandima Ratnayake, Arvind Keprate, and Xavier Ficquet. "Machine Learning Approach for Estimating Residual Stresses in Girth Welds of Topside Piping." In ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/omae2020-18703.

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Abstract Residual stresses are internal self-equilibrating stresses that remain in the component even after the removal of external load. The aforementioned stress when superimposed by the operating stresses on the offshore piping, enhance the chances of fracture failure of the components. Thus, it is vital to accurately estimate the residual stresses in topside piping while performing their fitness for service (FFS) evaluation. In the present work, residual stress profiles of girth welded topside sections of P91 pipes piping are estimate using a machine learning approach. The training and testing data for machine learning is acquired from experimental measurements database by Veqter, UK. Twelve different machine learning algorithms, namely, multi-linear regression (MLR), Random Forest (RF), Gaussian process regression (GPR), support vector regression (SVR), Gradient boosting (GB) etc. have been trained and tested. In order to compare the accuracy of the algorithms, four metrics, namely, Root Mean Square Error (RMSE), Estimated Variance Score (EVS), Maximum Absolute Error (AAE), and Coefficient of Determination (R^2) are used. Gradient boosting algorithm gives the best prediction of the residual stress, which is then used to estimate the residual stress for the simulated input parameter space. In the future work authors shall utilize the residual stress predictions from Gradient boosting algorithm to train the Bayesian Network, which can then be used for estimating less conservative through-thickness residual stresses distribution over a wide range of pipe geometries (radius to thickness ratio) and welding parameters (based on heat input). Furthermore, besides topside piping, the proposed approach finds its potential applications in structural integrity assessment of offshore structures, and pressure equipment’s girth welds.
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