Moisture content prediction of banana during drying process using artificial neural network

摘要：

In this study an artificial neural network was developed to predict the moisture content of banana during drying process.Thus, the experiments were performed at three levels of air temperature (70, 80, 90℃), two levels of air velocity (0.5 and 1 m/s), and two levels of thickness (3 and 5 mm).In the purposed artificial neural network, air temperature, air velocity, drying time and slices thickness were considered as inputs and moisture content was considered as output.In this study, feed forward network with the tansig and logsig transfer function and Levenbery- Marqwardt learning rule and multi-layer perceptron network with the tansig transfer function and momentum learning rule were used for modelling of experimental data.These networks were compared with nine various mathematical equations.The results showed that the feed forward network is better than multi-layer perceptron network and mathematical equations to predict the experimental data.

关键词： feed forward network mathematical equations tansig and logsig transfer function Levenbery- Marqwardt

作者: M.A.Ebrahimi S.S.Mohtasebi Sh.Rafiee S.Hoseinpoor M.Khanali

作者单位: Agricultural Machinery Engineering University of Tehran, Islamic Republic of Iran Mechanics Engineering ,University of Tehran, Islamic Republic of Iran Agricultural Machinery Engineering,University of Tehran, Islamic Republic of Iran Department of Bio-system Engineering, University College of Agriculture and Natural Resources, Unive

会议类型: 国际会议

会议名称: The 7th Asia-Pacific Drying Conference(第七届亚太地区干燥会议 ADC2011)

会议地点: 天津

会议语种:英文

页码: 1-5

在线出版日期: 2011-09-18（万方平台首次上网日期，不代表论文的发表时间）