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Genetic Algorithm-based Fuzzy Optimization Neural Network Model for WSNs Performance Evaluation
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- 발행기관
- 보안공학연구지원센터(IJFGCN) 바로가기
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- 간행물
- International Journal of Future Generation Communication and Networking 바로가기
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- 통권
- Vol.7 No.5 (2014.10)바로가기
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- 페이지
- pp.239-250
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A233170
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- In recent years, wireless sensor network is becoming the hottest research fields, which opens up a wealth of opportunities for many application domains. Comprehensive performance of WSNs is important for planning and management of a construction network. In order to deal with the deficiency of existing evaluation methods for WSNs performance, an intelligent evaluation model based on fuzzy optimization neural network model is proposed, which introduces genetic algorithm to optimize the connection weight of the neural network model to achieve approximate optimal solution. The weights are to be regarded as initial values for the next step that a neural network is tuned finely further. Fuzzy optimization model is as activation function of the neural network, therefore the model has explicit physical meanings. This model is applied to evaluate WSNs performance of some samples, and the comparative analysis with other models shows that the model improves evaluation precision and efficiency and is practical. The first part is the research status and related problems. The second part is the establishment of evaluation model. The last part is the experimental analysis and conclusion.
목차
Abstract
1. Introduction
2. Fuzzy Optimization Neural Network Model
2.1. Fuzzy Optimization Model
2.2. Neural Network Model
3. Improved Fuzzy Optimization Neural Network Model based on Genetic Algorithm
3.1. To Determine the Encoding Method
3.2. The Population Initialization
3.3. Fitness calculation and set other parameters of GA
3.4 The process of evaluation model
4. A Numerical Example
4.1. Select the Input and Output Parameters
4.2. Input and Output Data Preprocessing
4.3. Analysis of the results
5. Conclusion
Acknowledgements
References
키워드
WSNs fuzzy optimization neural network model Genetic algorithm저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJFGCN) [Science & Engineering Research Support Center, Republic of Korea(IJFGCN)]
- 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 Future Generation Communication and Networking
- 간기
- 격월간
- pISSN
- 2233-7857
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Future Generation Communication and Networking Vol.7 No.5
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