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	Provedor de dados CIGR Journal (1) Chilean J. Agric. Res. (1) Autor Akbarnia, Abbas (1) Alimardani, Reza (1) Amiri Chayjan,Reza (1) Esna-Ashari,Mahmood (1) Farhani, Foad (1) Mohammadi, Asghar (1) Mais... Palavra-chave Back propagation (2) Analysis of variance (1) Entropy (1) Force evaluation (1) Isosteric heat (1) Multi layer perceptron (1) Prediction (1) Sorption isotherm (1) Soya bean (1) Mais... Tipo do documento Info:eu-repo/semantics/article (1) Journal article (1) Ano 2014 (1) 2010 (1) País Chile (1) China (1) Idioma Inglês (2)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 2 Primeira ... 1 ... Última Simulation of draft force of winged share tillage tool using artificial neural network model CIGR Journal Akbarnia, Abbas; Mohammadi, Asghar; Alimardani, Reza; Farhani, Foad. An artificial neural network (ANN) model, with a back propagation learning algorithm, was developed to predict draft requirements of two winged share tillage tools in a loam soil. The input parameters to the 3–7–1 ANN model were; share width, working depth and operating speed. The output from the network was the draft requirement of each tillage tool. The developed model predicted the draft requirements of the winged share tillage tools with a mean relative error of less than 7% and mean square errors of less than 0.05, when compared to measured draft values. This result indicates that the ANN model had successfully learnt from the training data set to enable correct interpolation and could be used as an alternative tool for modeling soil-tool interaction... Tipo: Info:eu-repo/semantics/article Palavras-chave: Analysis of variance; Back propagation; Force evaluation; Multi layer perceptron; Prediction. Ano: 2014 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3022 Modeling Isosteric Heat of Soya Bean for Desorption Energy Estimation Using Neural Network Approach Chilean J. Agric. Res. Amiri Chayjan,Reza; Esna-Ashari,Mahmood. Sorption isotherm of soya bean (Glycine max (L.) Merr.) was obtained by the dynamic experimental method. Artificial Neural Networks (ANNs) were used for modeling soya bean equilibrium moisture content (EMC). Thermodynamic equations and trained ANN for prediction of two thermodynamic properties of net isosteric heat and entropy of soya bean were utilized. The ANN models were better compared with mathematical models. In this study, the isosteric heat and entropy of sorption of soya bean were separately predicted by two power models as a EMC function. Predictive power of the models was high (R² ≈ 0.99). At the moisture content above 11% (dry basis, db), isosteric heat and entropy of sorption of soya bean were smoothly decreased, while they were... Tipo: Journal article Palavras-chave: Back propagation; Entropy; Isosteric heat; Sorption isotherm; Soya bean. Ano: 2010 URL: http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-58392010000400012 Registros recuperados: 2 Primeira ... 1 ... Última

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