원문정보
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초록
영어
A method of feature extraction in non-stationary signals is proposed to improve the correct classification rates of plant electrical signals. The samples are composed of plant electrical signals datum which includes the period of four circumstances. For wavelets have the trait of arbitrary distinction and decomposition, eigenvector which reflect different state of plant electrical signals are extracted from different frequency segments with the technology of wavelet decomposition. Then we take them input neural network as samples to establish the model of BP neural network. Experimental results demonstrate that the classification accuracy of the proposed feature extraction method for experiment plant electrical signals is high.
목차
Abstract
1. Introduction
2. Basic Principles
2.1. Wavelet Decomposition Principle
2.2. Electric Signal of the Plant
2.3. BP Neural Network Establishment and Training
3. Energy Characterization Extraction Algorithm
4. Experimental Results and Analysis
5. Conclusion
Acknowledgments
References
1. Introduction
2. Basic Principles
2.1. Wavelet Decomposition Principle
2.2. Electric Signal of the Plant
2.3. BP Neural Network Establishment and Training
3. Energy Characterization Extraction Algorithm
4. Experimental Results and Analysis
5. Conclusion
Acknowledgments
References
저자정보
참고문헌
자료제공 : 네이버학술정보