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A Neural Network Approach to Predict Quality of Pellet Feed Basing on Materiel and Process Parameters
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2016 ASABE Annual International Meeting 162434576.(doi:10.13031/aim.20162434576)
Authors: Xiao Chen, Hongying Wang, Dandan Kong, Yan Yue, Fang Lv, Peng Fang
Keywords: guidance, production efficiency, feed processing, modeling, process optimization.
As a typical Black Box Approach, model established by Artificial Neural Network (ANN) is based on inside relation between inputs and outputs which overcome the limitations of traditional mathematical model facing the complexity of feed processing, avoiding interference of human factors in maximum. For this reason, the Back-Propagation algorithm was selected to modeling the processing of pellet feed and achieves quality prediction on account of effected parameters.
Basing on livestock feed, an indicator system consisted by inputs (processing parameters and diet character) as well as outputs (Pellet Durability Index and pellet hardness) was established. Meantime, the ANN model was actualized applying a toolbox in the MATLAB software using the Levenberg-Marquardt algorithm which shows high stability and fast convergence rate. After a preliminary experiment, the structure of the model was designed as four layers include input layer, output layer and two hidden layers with 7 and 8 neurons accordingly.
By analyzing results of the testing, it can be concluded that the utilization of ANN in predicting the product quality of pellet feed was feasible with relative ideal results. The application of this method is capable of saving time and money costing for proper processing parameters search as well as product quality fluctuation in a certain degree, which is meaningful to the profit growth of enterprise and the rational use of social resources.(Download PDF) (Export to EndNotes)