林喜娜,王相友,丁莹.双孢蘑菇远红外干燥神经网络预测模型建立[J].农业机械学报,2010,41(5):110-114.
.Experiment on Neural Network Prediction Modeling of Far Infrared Radiation Drying for Agaricus bisporus[J].Transactions of the Chinese Society for Agricultural Machinery,2010,41(5):110-114.
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双孢蘑菇远红外干燥神经网络预测模型建立   [下载全文]
Experiment on Neural Network Prediction Modeling of Far Infrared Radiation Drying for Agaricus bisporus   [Download Pdf][in English]
  
DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  双孢蘑菇  远红外  干燥  含水率  预测模型  BP神经网络
基金项目:
林喜娜  王相友  丁莹
山东理工大学
中文摘要:分析了双孢蘑菇在远红外干燥过程中,辐射强度、辐射距离、物料温度、物料厚度、干燥时间等因素对干燥速率的影响。基于BP神经网络建立了含水率与各因素之间的网络模型结构,输入层、隐含层和输出层的神经元数分别为5、11、1。以干燥试验数据作为训练和测试的样本值,利用Matlab中的神经网络工具箱,经过有限次迭代计算获得一个反映试验数据内在联系的数学模型,并实现对该模型的训练和系统的模拟。结果表明:在试验范围内,BP神经网络可以高效、准确、快速地建立模型,且模型的预测值与实测值拟合较好,能够准确而可靠地实现含水率在线预测。
Key Words:
Abstract:The factors influenced infrared radiation drying rates for Agaricus bisporus, such as radiation intensity, radiation distance, material temperature, material thickness and drying time were analyzed. The network model structure between moisture content and all the controlling factors was built based on feed-forward neural network, the selected structure of the applied neural network, with its five inputs, single output and 11 hidden neurons were used. All data series obtained from different drying runs were used for training and test, mathematical model responding to inner relationship of the experimental data was obtained by finite iteration calculation, and it was trained and simulated systemically by using Matlab neuralnetwork toolbox. It was concluded that the model could be built by the BP neural network, cost-effectively, accurately and rapidly during far infrared drying of Agaricus bisporus within the trial stretch. It was found that the predictions of the artificial neural network model fit the experimental data preferably, and the applications of the artificial neural networks could be used for the online state estimation moisture content with more suitable and accuracy.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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