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Research on Technological Innovation Talents of the Six Provinces of Mid-China based on BP Artificial Neural Networks of the Golden Section Theory
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- 발행기관
- 보안공학연구지원센터(IJHIT) 바로가기
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- 간행물
- International Journal of Hybrid Information Technology 바로가기
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- 통권
- Vol.7 No.6 (2014.11)바로가기
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- 페이지
- pp.143-154
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A235243
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Technological innovation talent is the core element of economic and social development. It is a symbol of a country's soft power reflected from the perspective of knowledge. Therefore it is theoretically valuable and practically significant for a country to evaluate the comprehensive level technological innovation. Based on the construction of evaluation index system of the technological innovation talents’ competitiveness, we will use BP neural network to evaluate in this paper. We determine the actual situation of the evaluation system according to the input layer, hidden layer and output layer of the model as well as the number of neurons in each layer. The number of neurons in the input layer is the number of indices of technological innovation talents evaluation. The output layer represents the comprehensive ability level and has only one neuron. However, it is difficult to figure out the number of hidden layers of BP neural networks. We use the golden section method to accurately select their layers. This can effectively avoid the problem of excessive training error due to the random selection of hidden layer. Then we illustrate the accuracy and usefulness of this algorithm with the case of the technological innovation talents of the six central provinces. Tests show that the comprehensive ability of technological innovation talents of Hubei is the most powerful in the six central provinces and is followed by Anhui. The remaining four provinces have similar levels. The actual development of technological innovation talents agree well with the experimental results.
목차
Abstract
1. Introduction
2. BP Network Optimization Model
3. Activation Function of BP Network
4. The Simulation and Conclusion
References
키워드
innovative talents Evaluation model The BP neural network The Golden Section method저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJHIT) [Science & Engineering Research Support Center, Republic of Korea(IJHIT)]
- 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
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- 간행물명
- International Journal of Hybrid Information Technology
- 간기
- 격월간
- pISSN
- 1738-9968
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Hybrid Information Technology Vol.7 No.6
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