کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6873272 1440632 2018 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Newton-Gauss curvature matrix based cDBN for online edible fungus drying prediction model
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
Newton-Gauss curvature matrix based cDBN for online edible fungus drying prediction model
چکیده انگلیسی
In 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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 81, April 2018, Pages 273-279
نویسندگان
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