원문정보
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초록
영어
Neural network has strong ability of pattern recognition. In consideration of the problems of the traditional pure BP neural network, such as subjecting to the randomness of initial weights, slow convergence speed, low efficiency, easy to fall into local extreme value, in this paper we proposing an optimal BP network fusing with the genetic algorithm using in bridge health assessment. The optimized BP network algorithm has a good diagnosis effect, and improves the calculation accuracy and speed of the identification of bridge structure damage.
목차
Abstract
1. Introduction
2. Hardware Composition and Function of the System
3. Genetic Algorithm to Optimize the BP Neural Network
3.1. The traditional BP neural network
3.2 The principle of genetic algorithm
3.3 Genetic algorithm optimizes the BP neural network
4. Project Instance Analysis
5. Conclusion
Acknowledgements
References
1. Introduction
2. Hardware Composition and Function of the System
3. Genetic Algorithm to Optimize the BP Neural Network
3.1. The traditional BP neural network
3.2 The principle of genetic algorithm
3.3 Genetic algorithm optimizes the BP neural network
4. Project Instance Analysis
5. Conclusion
Acknowledgements
References
저자정보
참고문헌
자료제공 : 네이버학술정보