NDLI: Universal Study on Parametric Models in Equipment System Cost Estimation

Content Provider	IEEE Xplore Digital Library
Author	Tiejun Jiang Shangfen Guo Shuzong Wang Ruxiang Wei
Copyright Year	2007
Description	Author affiliation: Naval Univ. of Eng., Wuhan (Tiejun Jiang; Shangfen Guo)
Abstract	Currently, in equipment system cost estimation, parametric models are applied widely. In this paper, on the base of the concrete analysis on the adopted cost estimation model, the discussion was given on the universal analytical procedure in equipment system cost estimation, and the establishment of objective functions and the determination of model parameters were analyzed in parametric models and a sort of modeling method was brought forward considering the fitting accuracy and generalization capability, finally a practical modeling problem was transformed into a nonlinear system optimization problem and particle swarm optimization (PSO) was adopted to obtain the model parameters aim at the complicated optimization objective function. All of those were analyzed through a case. Theoretical researches and computational analyses show that the proposed method has essentially solved the problems of model establishment and parameter optimization in parametric methods, has the good universality and strong generalization capability, and can be used as the appraisal standard of parameter cost estimation models and the basic of self-adapting modeling of parametric cost estimation.
Starting Page	5228
Ending Page	5231
File Size	106819
Page Count	4
File Format	PDF
ISBN	9781424413119
DOI	10.1109/WICOM.2007.1280
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Parameter estimation Weapons Engineering management Neural networks Optimization methods Cost function Parametric statistics Regression analysis Mathematical model Particle swarm optimization
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

System Identification Based on Particle Swarm Optimization Algorithm

Software effort estimation using machine learning methods

Parameter estimation for asymptotic regression model by particle swarm optimization

Cost Prediction of Equipment System Using LS-SVM with PSO

Cutting Parameter Optimization Based on Particle Swarm Optimization

Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization

Particle swarm optimization for chaotic system parameter estimation

Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization

Parameter estimation of three-parameter weibull distribution via particle swarm optimization algorithm

Universal Study on Parametric Models in Equipment System Cost Estimation

Similar Documents

System Identification Based on Particle Swarm Optimization Algorithm

Software effort estimation using machine learning methods

Parameter estimation for asymptotic regression model by particle swarm optimization

Cost Prediction of Equipment System Using LS-SVM with PSO

Cutting Parameter Optimization Based on Particle Swarm Optimization

Nonlinear System Identification Based on Radial Basis Function Neural Network Using Improved Particle Swarm Optimization

Particle swarm optimization for chaotic system parameter estimation

Improving the accuracy in software effort estimation: Using artificial neural network model based on particle swarm optimization

Parameter estimation of three-parameter weibull distribution via particle swarm optimization algorithm

Universal Study on Parametric Models in Equipment System Cost Estimation