Identification of nonwoven uniformity using generalized Gaussian density and fuzzy neural network |
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Authors: | Jianli Liu Baoqi Zuo Philippe Vroman Besoa Rabenasolo Xianyi Zeng |
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Affiliation: | 1. School of Textiles and Clothing , Jiangnan University , Wuxi 214122, China liufengy1@yahoo.com.cn;3. National Engineering Laboratory for Modern Silk , Soochow University , Suzhou 215021, China;4. Université Lille Nord de France , Lille F-59000, France;5. ENSAIT , GEMTEX , Roubaix F-59056, France |
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Abstract: | A joint method to identify nonwoven uniformity by combining wavelet transform, generalized Gaussian density (GGD) and generalized dynamic fuzzy (GDF) neural network is presented in this paper. Six hundred and twenty‐five nonwoven images of five different grades, 125 images of each grade, are decomposed at three different levels with coif4 wavelet base. Wavelet coefficients in each subband are independently modeled by GGD model, while the scale and shape parameters of that are extracted as input features of GDF neural network. For comparison, two energy‐based features are also extracted from wavelet coefficients directly, the number of which is the same as the scale and shape parameters estimated from GGD model with maximum likelihood (ML) estimator. Experimental results on the 625 nonwoven images indicate the GGD model parameters are more expressive and powerful in characterizing textures than the energy‐based ones. The proposed method has high identification accuracy, such as when the images are decomposed at Level 3 and described with GGD model parameters, the identification accuracies of five grades are all 100%. Additionally, to reduce the redundancy of the generated fuzzy rules, an effective complementary approach, fuzzy rule base reduction based on ‘CityBlock’ distance is proposed. |
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Keywords: | nonwovens surface uniformity two‐dimensional discrete wavelet transform generalized Gaussian density maximum likelihood estimator shape parameter generalized dynamic fuzzy neural network |
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