ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

The identification of six kinds of Chinese spirits, including similar quality using electronic nose is a complex and difficult work. In order to enhance the correct identification rate of six kinds of Chinese liquors using electronic nose (E-nose), a Fisher discriminant analysis (FDA) method based on kernel entropy component analysis (KECA) was introduced. Based on this method, the influence of different features combination representation types of E-nose signals on the discrimination result of six kinds of Chinese liquors was studied in-depth. Firstly, integral value (INV), variance (VAR), relative steady-state average value (RSAV), average differential value (ADV) and wavelet energy value (WEV) of E-nose signals were extracted as five kinds of feature values, and the FDA result of each single feature showed that the identification result based on INV, AVRS and WEV was superior to that of the other two features, respectively. Thus the features INV, AVRS and WEV were selected as subsequent analysis features. Then, for the features of INV, AVRS and WEV, when the E-nose signals were represented by random combinations based on two features or three features combination, FDA results displayed that the identification results of multi-features combinations were better than that of single feature, especially the three features combination was the best. Finally, on the premise of combining the three features to represent electronic nose signals, and the discrimination result of six kinds of Chinese liquors was deeply investigated by an introduced KECA+FDA. When the radial basis function (RBF) was selected as kernel transform function, with the help of a measuring method of matrix similarity based on Euclidean distance, the characteristic parameter of RBF was defined, which was 16.8608. And the correct identification rate of the test set samples was from 79.92% of FDA up to 100% of the KECA+FDA. Meanwhile, the discrimination result of KECA+FDA was better than that of BP neural network and support vector machine. This indicated that the KECA+FDA method can effectively improve the identification ability of the six kinds of Chinese liquors;at the same time, it also provided a feasible pattern recognition method for the identification of complex samples such as Chinese liquors by electronic nose in the future.