Strategies for constructive neural networks and its application to regression models

2012 ◽  
Author(s):  
Jifu Nong
Keyword(s):  
Neural Networks ◽  
Regression Models ◽  
Constructive Neural Networks

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Online filtering of CO2 signals from a bioreactor gas outflow using a committee of constructive neural networks

2007 ◽  
Vol 31 (2) ◽  
pp. 101-109 ◽  
Author(s):  
Roberto C. Giordano ◽  
João R. Bertini ◽  
Maria C. Nicoletti ◽  
Raquel L. C. Giordano
Keyword(s):  
Neural Networks ◽  
Constructive Neural Networks

scholarly journals ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

2018 ◽  
Vol 36 (4) ◽  
pp. 891
Author(s):  
Ouorou Ganni Mariel GUERA ◽  
José Antônio Aleixo SILVA ◽  
Rinaldo Luiz Caraciolo FERREIRA ◽  
Héctor Barrero MEDEL ◽  
Daniel Álvarez LAZO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Regression Models ◽  
Growth Models ◽  
Pinus Caribaea ◽  
Three Stages ◽  
Artificial Neural ◽  
Sigmoidal Growth ◽  
Pinus Caribaea Morelet

The present study was carried out to compare the performances of regression models and Artificial Neural  Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type  and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which  Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three  stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

scholarly journals Mexican Stock Exchange Performance after the Crisis of 2008: Application of Data Mining

2020 ◽  
Vol 18 ((1)) ◽  
Author(s):  
Eliseo Ramírez Reyes ◽  
Arturo Morales Castro ◽  
Néstor Juan Sanabria Landazábal
Keyword(s):  
Neural Networks ◽  
Default Risk ◽  
Regression Models ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sovereign Default ◽  
Linear Regression Models ◽  
Exchange Performance ◽  
Sovereign Default Risk ◽  
Dow Jones Industrial Average

Different prediction models are explored to analyze the performance of the Mexican Stock Exchange (PQI) after the 2008 crisis. These models have demonstrated a good prognostic capacity for both multivariable and univariable approaches given their non-parametric characteristics. The selected variables were: Dow Jones Industrial Average Index (DJIA), CPI, International Reserves (IR), CETES28, USDMX exchange rate, (M1) and the sovereign default risk of Mexico (MRDS). The models were evaluated with MAPE and compared with linear regression models (LR) and neural networks (NN). The results show that the models have a similar performance according to the percentages of error they presented.

scholarly journals Neural Networks for the Joint Development of Individual Payments and Claim Incurred

Risks ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 33
Author(s):  
Łukasz Delong ◽  
Mario V. Wüthrich
Keyword(s):  
Neural Networks ◽  
Regression Models ◽  
Development Process ◽  
Real Data ◽  
Data Set ◽  
Joint Development ◽  
Chain Ladder Method ◽  
History Of ◽  
Feature Information ◽  
Chain Ladder

The goal of this paper is to develop regression models and postulate distributions which can be used in practice to describe the joint development process of individual claim payments and claim incurred. We apply neural networks to estimate our regression models. As regressors we use the whole claim history of incremental payments and claim incurred, as well as any relevant feature information which is available to describe individual claims and their development characteristics. Our models are calibrated and tested on a real data set, and the results are benchmarked with the Chain-Ladder method. Our analysis focuses on the development of the so-called Reported But Not Settled (RBNS) claims. We show benefits of using deep neural network and the whole claim history in our prediction problem.

scholarly journals Dynamical Modeling and Excitation Reconstruction as Fundamental of Earth Rotation Prediction

2010 ◽  
Vol 45 (2) ◽  
pp. 95-106 ◽  
Author(s):  
L. Zotov
Keyword(s):  
Neural Networks ◽  
Angular Momentum ◽  
Earth Rotation ◽  
Regression Models ◽  
Chandler Wobble ◽  
Tidal Effects ◽  
Dynamical Modeling ◽  
Earth Orientation Parameters ◽  
Key Issues ◽  
Orientation Parameters

Dynamical Modeling and Excitation Reconstruction as Fundamental of Earth Rotation Prediction Though pure mathematical approximations such as regression models and neural networks show good results in Earth rotation forecasting, dynamical modeling remains the only base for the physically meaningful prediction. That assumes the knowledge of cause-effect relationships and physical model of the rotating Earth. Excitation reconstruction from the observed Earth orientation parameters (EOP) is a crucial stage, needed for comparison with known causes, such as tidal forcing, atmospheric (AAM), oceanic (OAM) angular momentum changes, and uncovering unknown ones. We demonstrate different approaches, which can be used to avoid ill-conditionality and amplification of noises during the inversion. We present amplitude and phase studies of the model and reconstructed excitations of Chandler wobble. We found out, that modulation of Chandler excitation is synchronous with 18-yr tidal effects in the Earth's rotation rate changes. The results of the study can be used for excitation and EOP forecast. The key issues of the EOP prediction are discussed.

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