UC知道跪求：十万火急 英文文献翻译
来源：百度知道 编辑：UC知道 时间：2024/05/14 04:27:52
A Close To Real-time Prediction Method Of Total Coliform Bacteriain Foods Based On Image Identification Technology And Artificial Neural Network
A prediction method of total coliform bacteria based on image identification technology in foods was proposed. In order to get the close to real-time detection results, this method used the total count of bacteria and bacilli to predict the total coliform bacteria counts because conforms are difficult to extract the feature parameters to be recognized and enumerated, while total count of bacteria and bacilli could be enumerated by using image identification technology. An optimal artificial neural network (ANN) model was presented for prediction of total coliform bacteria counts. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consisted two hidden layers with five neurons in each hidden layer. Results showed that predicted total coliform bacteria counts were positively correlated
A prediction method of total coliform bacteria based on image identification technology in foods was proposed. In order to get the close to real-time detection results, this method used the total count of bacteria and bacilli to predict the total coliform bacteria counts because conforms are difficult to extract the feature parameters to be recognized and enumerated, while total count of bacteria and bacilli could be enumerated by using image identification technology. An optimal artificial neural network (ANN) model was presented for prediction of total coliform bacteria counts. Several configurations were evaluated while developing the optimal ANN model. The optimal ANN model consisted two hidden layers with five neurons in each hidden layer. Results showed that predicted total coliform bacteria counts were positively correlated
密切实时预测方法大肠菌群Bacteriain食品基于图像识别技术和人工神经网络
预测方法的总大肠菌群基于图像识别技术在食品中有人提议。为了获得接近实时检测结果，这种方法使用了细菌总数和细菌总数的预测，因为大肠菌群数符合难以提取特征参数，以得到承认和列举，而细菌总数和杆菌可以列举的使用图像识别技术。最佳的人工神经网络（ ANN ）模型，预测的总大肠菌群数。一些配置进行了评估，而发展中国家的最优神经网络模型。神经网络模型的优化包括两个隐藏层神经元的每个五年隐层。预测结果表明，总大肠菌群数呈正相关实验总大肠菌群数获得传统多管发酵工艺（相关系数〜 2 = 0.9716 ） ，其中预测精度明显优于其他预测模型（相关系数的线性回归模型，二阶多项式回归模型和多项式趋势面分析是39.81 ％ ， 67.17 ％和78.85 ％ ，分别） 。
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