24 October 2017 Study on pattern recognition of Raman spectrum based on fuzzy neural network
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Proceedings Volume 10461, AOPC 2017: Optical Spectroscopy and Imaging; 104611F (2017) https://doi.org/10.1117/12.2285367
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Hydatid disease is a serious parasitic disease in many regions worldwide, especially in Xinjiang, China. Raman spectrum of the serum of patients with echinococcosis was selected as the research object in this paper. The Raman spectrum of blood samples from healthy people and patients with echinococcosis are measured, of which the spectrum characteristics are analyzed. The fuzzy neural network not only has the ability of fuzzy logic to deal with uncertain information, but also has the ability to store knowledge of neural network, so it is combined with the Raman spectrum on the disease diagnosis problem based on Raman spectrum. Firstly, principal component analysis (PCA) is used to extract the principal components of the Raman spectrum, reducing the network input and accelerating the prediction speed and accuracy of Network based on remaining the original data. Then, the information of the extracted principal component is used as the input of the neural network, the hidden layer of the network is the generation of rules and the inference process, and the output layer of the network is fuzzy classification output. Finally, a part of samples are randomly selected for the use of training network, then the trained network is used for predicting the rest of the samples, and the predicted results are compared with general BP neural network to illustrate the feasibility and advantages of fuzzy neural network. Success in this endeavor would be helpful for the research work of spectroscopic diagnosis of disease and it can be applied in practice in many other spectral analysis technique fields.
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Xiangxiang Zheng, Xiangxiang Zheng, Xiaoyi Lv, Xiaoyi Lv, Jiaqing Mo, Jiaqing Mo, } "Study on pattern recognition of Raman spectrum based on fuzzy neural network", Proc. SPIE 10461, AOPC 2017: Optical Spectroscopy and Imaging, 104611F (24 October 2017); doi: 10.1117/12.2285367; https://doi.org/10.1117/12.2285367


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