scholarly journals Integrated Model of Demand for Telephone Services in Terms of Microeconometrics

2016 ◽  
Vol 16 (2) ◽  
pp. 72-83
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Integrated Model ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Telecommunications Network ◽  
Short Term Forecasting ◽  
Empirical Material

Abstract The paper presents the results of the testing effectiveness of the integrated model in the short-term forecasting of demand for telephone services in 24-hour cycles. The linear regression model with dichotomous (binary) independent variables was integrated with the feed forward neural network. The regression model was used as a filter of modelled variability of the demand. The neural network was used to model residual variability. The research shows that the integrated model has a higher possibility of approximation and prediction in comparison to the non-integrated linear regression model. The research study was based on data provided by the selected telecommunications network operator. The range of empirical material consisted of hourly counted seconds of outgoing calls and generated by network subscribers in various analytical sections.

scholarly journals Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services

2018 ◽  
Vol 18 (2) ◽  
pp. 159-177
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Research Work ◽  
Neural Model ◽  
Integrated Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Network Application ◽  
Telecommunications Services ◽  
Short Term Forecasting

Abstract The aim of this research study is to test the effectiveness of the single-sectional integrated model, in which a neural network is applied to support a regression, as a consistent tool for short-term forecasting of hourly demand (in sec.) for telecommunications services. The theoretical part of the paper involves the idea of the single-sectional integrated model and differences between this model and a multi-sectional integrated model. Moreover, the research methodology is described, i.e. the elements used in the constructed model (the feedforward neural model and the regression with dichotomous explanatory variables), and the manner of their integration are discussed. In the empirical part of this work, the results of the carried out experiments are included. The comparison of the obtained effectiveness (in terms of approximation and prediction) of the explored single-sectional integrated model with the effectiveness of the non-supported regression model and the multi-sectional integrated model are conducted. In this research work, it is proved that the single-sectional integrated model enables better results in comparison to the non-integrated regression and the mutli-sectional integrated model. The originality of this paper is based on: the created single-sectional integrated model in terms of the analysed phenomenon, the verification of the model effectiveness, and the comparison of the constructed model with other models and assessment.

City Manager Compensation and Performance

2013 ◽  
pp. 325-332
Author(s):  
Jean X. Zhang
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Regression Model ◽  
Nonlinear Model ◽  
Linear Regression Model ◽  
Mean Squared Error ◽  
City Manager ◽  
Squared Error ◽  
And Performance ◽  
Manager Compensation

This chapter proposes a nonlinear artificial Higher Order Neural Network (HONN) model to study the relation between manager compensation and performance in the governmental sector. Using a HONN simulator, this study analyzes city manager compensation as a function of local government performance, and compares the results with those from a linear regression model. This chapter shows that the nonlinear model generated from HONN has a smaller Root Mean Squared Error (Root MSE) of 0.0020 as compared to 0.06598 from a linear regression model. This study shows that artificial HONN is an effective tool in modeling city manager compensation.

Study on State Identifying of Rotary Machine Torsional Oscillation Based on Wavelet Neural Network

2010 ◽  
Vol 26-28 ◽  
pp. 211-217
Author(s):  
Zong Meng ◽  
Feng Jie Fan ◽  
Bin Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Torsional Oscillation ◽  
Wavelet Neural Network ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Rotary Machine

This article established a new combining hierarchy genetic algorithm and multivariate linear regression model of WNN (wavelet neural network) for identify the feature of rotary machine. The effection on the question of nonlinear approximation is verified through the simulation and optimization. The test datas of a tandem mill are inputted into the model. After trained, the model has automatic ability of obtained the inspect information and the ability of adapt the changing of worked condition. The self-adaptive study and diagnosis of torsional oscillation state on different work condition are realized. The results verify the combining hierarchy genetic algorithm and multivariate linear regression model has the reliability.

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