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首页> 外文期刊>Future generation computer systems >Newton-Gauss curvature matrix based cDBN for online edible fungus drying prediction model
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Newton-Gauss curvature matrix based cDBN for online edible fungus drying prediction model

机译：基于牛顿-高斯曲率矩阵的cDBN在线食用菌干燥预测模型

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摘要

AbstractIn order to solve the problem of moisture measurement in the edible fungus production, which affect the drying process of edible fungus, here proposed the matrix based continuous belief network for on-line edible fungus drying prediction system designing. Firstly, here perform unsupervised learning process for the depth of the belief network with input signal of edible fungus acquisition, and extract the information feature of edible fungus based on continuous transmission, then realize the network weight training with conjugate gradient, after that here perform the deduction for the stability of the continuous depth of the belief network, which ensure the stability of the network output data; Then, the Newton–Gauss curvature matrix optimization method is used to replace the traditional error back propagation method, which take the local optimization of the network hidden layer weights, and realize the fast convergence and improvement of the convergence accuracy; Finally, the Lorenz function training was used to verify the validity of the matrix continuous depth belief network, and the results showed that the algorithm could improve the rate of finished products.Highlights•A novel Newton–Gauss curvature matrix optimisation method is used.•Lorenz function training was used to verify the validity of the matrix.•The method realise fast convergence and improvement of the convergence accuracy.
机译： 摘要 为了解决食用菌生产中水分测量问题，该问题影响食用菌的干燥过程，在此提出了基于矩阵的连续置信网络线食用菌干燥预测系统设计。首先，利用食用菌获取的输入信号对置信网络的深度进行无监督学习过程，并基于连续传输提取食用菌的信息特征，然后利用共轭梯度实现网络权重训练，然后进行推导信念网络连续深度的稳定性，从而保证网络输出数据的稳定性；然后，使用牛顿-高斯曲率矩阵优化法代替传统的误差反向传播法，对网络隐层权重进行局部优化，实现了快速收敛和收敛精度的提高。最后，通过Lorenz函数训练验证了矩阵连续深度置信网络的有效性，结果表明该算法可以提高成品率。 突出显示 • 使用一种新颖的牛顿-高斯曲率矩阵优化方法。 < ce：list-item id =“ d1e276”> • Lorenz函数训练用于验证矩阵的有效性。 • 方法实现

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