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Evaluation of optimization techniques in predicting optimum moisture content reduction in drying potato slices 被引量:1
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作者 Onu Chijioke Elijah K.Igbokwe Philomena +2 位作者 T.Nwabanne Joseph O.Nwajinka Charles E.Ohale Paschal 《Artificial Intelligence in Agriculture》 2020年第1期39-47,共9页
The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems... The use of artificial intelligence models in predicting the moisture content reduction in the drying of potato(Ipomoea batata)sliceswas the focus of thiswork.The models used were adaptive neuro fuzzy inference systems(ANFIS),artificial neural network(ANN)and response surface methodology(RSM).The parameters considered were drying time,drying air speed and temperature.The capability and sensitivity analysis of the three models were evaluated using the correlation coefficient(R2)and some statistical error functions such as the average relative error(ARE),root mean square error(RMSE),Hybrid Fractional Error Function(HYBRID)and absolute average relative error(AARE).The result showed that the three models demonstrated significant predictive behaviourwith R2 of 0.998,0.997 and 0.998 for ANFIS,ANN and RSMrespectively.The calculated error functions of ARE(RSM=1.778,ANFIS=1.665 and ANN=4.282)and RMSE(RSM=0.0273,ANFIS=0.0282 and ANN=0.1178)suggested good harmony between the experimental and predicted values.It was concluded that though the three models gave adequate predictions that were in good agreement with the experimental data,the RSM and ANFIS gave better model prediction than ANN. 展开更多
关键词 Moisture content POTATO Adaptive neuro fuzzy inference systems Artificial neural network Response surface methodology
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