Fuzzy regression model of R&D project evaluation

Authors:
Shinji Imoto;Yoshiyuki Yabuuchi;Junzo Watada
Affiliations:
Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan;Shimonoseki City University, 2-1-1 Daigaku, Shimonoseki, Yamaguchi 751-8510, Japan;Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan
Venue:
Applied Soft Computing
Year:
2008

Citing 1
Cited 4

Possibilistic linear systems and their application to the linear regression model

Fuzzy Sets and Systems

A variable spread fuzzy linear regression model with higher explanatory power and forecasting accuracy

Information Sciences: an International Journal
Application of possibilistic fuzzy regression for technology watch

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS’2009
An integrated decision framework for evaluating and selecting e-learning products

Applied Soft Computing
Fuzzy linear regression based on Polynomial Neural Networks

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Engineering and technology play an important role in strengthening the competitive power of a company and in surviving a severe competition in the world. About 70% of the total R&D investment in Japan comes from the private sector. It is the most important to decide which research projects have to be adopted for a future research out of proposals from divisions and sections in a company. The objective of this paper is to analyze the results of experts' evaluation in selecting submitted proposals for R&D and to model the experts' evaluation. This paper analyzes a research and development of a certain manufacturing company in a heavy metallurgy industry. We employed a principal component model, dual scaling, AHP and fuzzy regression analysis to analyze the results that experts evaluated proposed research projects for single or plural of fiscal years. The experts' evaluation was pursued on the basis of (1) the objective of a research project, (2) its background, (3) its research contents, (4) the expected effect, (5) the possibility of obtaining patents, (6) project schedule, (7) developing cost, etc. The obtained model results in the same selection of projects as the experts did.