Abstract
In order to realize the fast and nondestructive detection, we propose a detection method based on Near Infrared Spectroscopy and Elman Neural Network. Based on the preprocessing Near Infrared Spectroscopy data of cashmere and wool, we analyze the principal components of the data, and build the detection model of cashmere and wool with Elman Neural Network. From the detection application of cashmere and wool, we propose a Variable Structure Hybrid Genetic Elman ANN prediction and modeling method in which an improved hybrid genetic algorithm is used to synchronously and dynamically optimize network structure, weights and self-feedback gains. The experiments based on the data of cashmere and wool from various districts demonstrate that the method combining Near Infrared Spectroscopy, Principal Components Analysis and Variable Structure Hybrid Genetic Elman ANN is a nondestructive detection method for cashmere and wool, and it can rapidly build high-accuracy detection models of cashmere and wool.
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Guo, F., Shang, S., Qi, M. (2011). Study of Detection Method of Cashmere and Wool Based on Near Infrared Spectroscopy and Elman Neural Network. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_44
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DOI: https://doi.org/10.1007/978-3-642-23896-3_44
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