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	Provedor de dados CIGR Journal (2) AgEcon (1) Autor Azadbakht, Mohsen (1) Farhadi, Payam (1) Ghajarjazi, Ehsan (1) Golmohammadi, Abdollah (1) Limsombunchai, Visit (1) Shahgholi, Gholamhossein (1) Sharifi Malvajerdi, Ahmad (1) Torshizi, Mohammad Vahedi (1) Ziaratban, Armin (1) Mais... Palavra-chave Artificial Neural Network (ANN) (3) C53 (1) Canola (1) Contact area (1) Contact volume (1) Cutting energy (1) Environmental Economics and Policy (1) Hedonic Model (1) House Price. (1) L74 (1) Land Economics/Use (1) Power consumption (1) Research Methods/ Statistical Methods (1) Rolling resistance (1) Shear strength (1) Stem (1) Three-dimensional footprint (1) Mais... Tipo do documento Info:eu-repo/semantics/article (2) Conference Paper or Presentation (1) Ano 2019 (1) 2016 (1) 2004 (1) País China (2) United States (1) Idioma Inglês (3)		Ordenar por:  Relevância Autor Título Ano Registros recuperados: 3 Primeira ... 1 ... Última House Price Prediction: Hedonic Price Model vs. Artificial Neural Network AgEcon Limsombunchai, Visit. The objective of this paper is to empirically compare the predictive power of the hedonic model with an artificial neural network model on house price prediction. A sample of 200 houses in Christchurch, New Zealand is randomly selected from the Harcourt website. Factors including house size, house age, house type, number of bedrooms, number of bathrooms, number of garages, amenities around the house and geographical location are considered. Empirical results support the potential of artificial neural network on house price prediction, although previous studies have commented on its black box nature and achieved different conclusions. Tipo: Conference Paper or Presentation Palavras-chave: Hedonic Model; Artificial Neural Network (ANN); House Price.; Environmental Economics and Policy; Land Economics/Use; Research Methods/ Statistical Methods; C53; L74. Ano: 2004 URL: http://purl.umn.edu/97781 Prediction of the tractor tire contact area, contact volume and rolling resistance using regression model and artificial neural network CIGR Journal Farhadi, Payam; Golmohammadi, Abdollah; Sharifi Malvajerdi, Ahmad; Shahgholi, Gholamhossein. A novel method to estimate the contact area and contact volume was developed with molding the tire footprint by liquid plaster and converting these molds to three-dimensional models using a 3D scanner. A 12.4-28, 6 ply tractor tire was operated under three levels of vertical load, three levels of inflation pressure and three levels of soil moisture content. To analyses the obtained data regression and Artificial Neural Network (ANN) models were used and the accuracy of predicted results were compared with measured data. A multi-layer perceptron feed-forward ANN with back propagation (BP) learning algorithm was employed. Two hidden layers were used in network architecture and the best number of neuron for each hidden layer was selected with attention to... Tipo: Info:eu-repo/semantics/article Palavras-chave: Artificial Neural Network (ANN); Contact area; Contact volume; Rolling resistance; Three-dimensional footprint. Ano: 2019 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/5438 Application of Artificial Neural Network (ANN) in predicting mechanical properties of canola stem under shear loading CIGR Journal Azadbakht, Mohsen; Torshizi, Mohammad Vahedi; Ziaratban, Armin; Ghajarjazi, Ehsan. In this study, at first the shear parameters including the maximum shear force, shear strength, shear energy and power consumption of canola stem were calculated through force-deformation curve; and then these mechanical properties were determined and predicted using artificial neural network.   For the tests, testing machine Instron (Model Santam STM-5) with 50 N load cell was used.  Stems were cut at 3 diameter levels (1 to 3, 3 to 5 and more than 5 mm), 3 cutting speed levels (75, 115 and 150 mm/min ), 3 cutting angles (0°, 30° and 60°) and three replicates.   Cutting parameters including maximum cutting force, shear strength; cutting energy; consumed power and cutting work were examined.  Tests lasted for each stem until the full cut.  Data... Tipo: Info:eu-repo/semantics/article Palavras-chave: Canola; Cutting energy; Stem; Shear strength; Power consumption; Artificial Neural Network (ANN). Ano: 2016 URL: http://www.cigrjournal.org/index.php/Ejounral/article/view/3785 Registros recuperados: 3 Primeira ... 1 ... Última

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