A Study on Classification of Food Texture with Recurrent Neural Network
A Study on Classification of Food Texture with Recurrent Neural Network
复制标题
循环神经网络食品质地分类的研究
DOI：
10.1007/978-3-319-43506-0_21
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Fumio Kojima
中科院分区：
文献类型：
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作者：
Shuhei Okada;Hiroyuki Nakamoto;Futoshi Kobayashi;Fumio Kojima
本研究利用具有2种感测元件的食物质构感测器，建构一食物质构评估系统。利用仿人牙齿结构的食物纹理传感器，将食物的特征数字化。食品分类是由递归神经网络进行的。递归神经网络接收来自食物质地传感器的时间序列输出，并输出分类信号。在实验中，使用递归神经网络对3种食品进行分类。
This study constructs a food texture evaluation system using a food texture sensor having sensor elements of 2 types. Characteristics of food are digitized by using the food texture sensor in imitation of the structure of the human tooth. Classification of foods is carried out by the recurrent neural network. The recurrent neural network receives the time-series outputs from the food texture sensor, and outputs classification signals. In the experiment, 3 kinds of food are classified by the recurrent neural network.