Artificial neural networks model for predicting excavator productivity
作者： C.M. TAM , THOMAS K.L. TONG , SHARON L. TSE
DOI: 10.1108/EB021238
关键词: Excavator 、 Artificial neural network 、 Productivity 、 Feed forward 、 Engineering 、 Machine learning 、 Quantitative model 、 Modelling methods 、 Artificial intelligence
摘要: This paper aims to develop a quantitative model for predicting the productivity of excavators using artificial neural networks (ANN), which is then compared with multiple regression developed by Edwards & Holt (2000). A network architecture multilayer feedforward (MLFF) used excavators. Finally, modelling methods, predictive behaviours and advantages each are discussed. The results show that ANN suitable mapping non‐linear relationship between excavation activities performance It concludes an ideal alternative estimating
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