현재 위치
Home
Prediction of Educational Investment in China Based on Artificial Neural Networks and Grey Model GM (1,1)
-
- 발행기관
- 보안공학연구지원센터(IJUNESST) 바로가기
-
- 간행물
- International Journal of u- and e- Service, Science and Technology 바로가기
-
- 통권
- Vol.8 No.12 (2015.12)바로가기
-
- 페이지
- pp.359-368
-
- 저자
-
- 언어
- 영어(ENG)
-
- URL
- https://www.earticle.net/Article/A270242
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Recent years, as China is experiencing a rapid development, the educational investments are undergoing a sharp increase compared to past decades. Educational investment has become a crucial aspect that deeply influencing the future development of a country. What's more importantly, knowing the future investments of education is a crucial affair that decides the future tendency of related projects such as government management, cooperation investment and personnel training. Decision makers cannot make any effective decisions without knowing the future investments of education under all these circumstances. However, it is fairly difficult for people to obtain the predicted data of educational investments without the aid of mathematical and computational modeling. Therefore, in this article, we aim at presenting a series of solutions for the prediction of educational investment for China based on multiple linear regression (MLR), artificial neural networks (ANNs) and grey model GM (1,1). Multiple comparisons are made for deciding whether model should be used under different external conditions. Our research successfully shows that all these models are available for practical applications and scientists and other related people can choose their suitable models alternatively for the sake of making a better prediction under different circumstances.
목차
Abstract
1. Introduction
2. Artificial Neural Network
3. Grey Model GM (1,1)
4. Results and Discussion
4.1. Development of the MLR Model
4.2. Development of ANN Models
4.3. Development of Grey Model GM (1,1)
5. Conclusion
References
키워드
Educational investment Chinese education multiple linear regression grey model GM (11) artificial neural networks저자
간행물 정보
발행기관
-
- 발행기관명
- 보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
-
- 간행물명
- International Journal of u- and e- Service, Science and Technology
- 간기
- 격월간
- pISSN
- 2005-4246
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.8 No.12
피인용수 : 0건 (자료제공 : 네이버학술정보)
함께 이용한 논문 이 논문을 다운로드한 분들이 이용한 다른 논문입니다.
출처 : 네이버학술정보
0개의 논문이 장바구니에 담겼습니다.