基于聚核模糊分类的多指标水蜜桃成熟度判别

针对水蜜桃成熟度判别的模糊性和不确定性,提出基于聚核模糊分类的多维指标水蜜桃成熟度判别方法。首先,选择与水蜜桃成熟相关的出汁率、糖度、硬度和失重率指标,构建多维指标数据集。其次,根据数据集分布和模糊区域重叠度,建立半梯半岭型隶属度函数。最后,利用熵值法计算多维指标输出权重集,并引入聚核权向量组融合相邻成熟阶段信息,建立基于聚核模糊分类的多维指标水蜜桃成熟度判别模型。结果表明,影响水蜜桃成熟度的多维指标权重由大到小依次为:糖度、出汁率、硬度和失重率;所提模型能较为准确地识别水蜜桃成熟等级,正确率为93.75%;对比常见的三角、梯形隶属度函数和最大隶属度法,聚核模糊分类能够提高判别正确率2.08%~12.50%。该结果为水蜜桃食品加工提供科学可靠的品质划分依据,能够提高食品加工效率、保障桃加工产品品质。

Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction

In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles.