Multilayer feedforward networks are universal approximators
Neural Networks
Neural Computation
MLP in layer-wise form with applications to weight decay
Neural Computation
A method to determine the required number of neural-network training repetitions
IEEE Transactions on Neural Networks
Yield prediction in apples using Fuzzy Cognitive Map learning approach
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
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The objective of this work was to optimize a neural network (NN) for modelling potato tuber growth and its in-field variations in eastern Canada. In addition to climatic inputs, the cumulative and maximal leaf area index (LAI) were incorporated to account for in-field scale variability. Soil and genetic parameters were assumed to be integrated in LAI as suggested by earlier work. Each input and combination of inputs was evaluated from the changes they induced in MAE (mean absolute error) and RMSE (root mean square error). Results using data from several replicated on-farm experiments between 2005 and 2008 suggest that a NN model using cumulative solar radiation, cumulative rainfall and cumulative LAI can adequately model site-specific tuber growth. The MAE of the retained model was 209kgDMha^-^1, which represents less than 4% of the mean final tuber yield for the 3 years of the study. Non-linear effects of explicative variables on tuber yield were attested by comparing the results of the NN simulations to those of a multiple linear regression (MLR). The failure of MLR to simulate temporal discontinuities in tuber growth supports the use of a non-linear approach such as a NN to model tuber growth.