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The following models were used for prediction: artificial neural networks (ANN), including linear networks (LN), multilayer perceptrons with one (MLP1) and two (MLP2) hidden layers and radial basis function (RBF) networks, decision trees, including classification and regression trees (CART) (Breiman et al., 1984) and chi-square automatic interaction detector (CHAID) (Kass, 1980) as well as multivariate adaptive regression splines (MARS) (Friedman, 1991).
As far as AIC is concerned, the best model quality was characteristic of MLP2 (TR, GL and FR), CART (SPC, LI and BE), CHAID (SPC and BE) or MARS (SP).
The most influential predictor of SP was LW (LN, MLP1, MLP2, CHAID and MARS) or MOL (RBF).
The following types of ANN were investigated in the present study: linear network (LN), multilayer perceptron with one hidden layer (MLP1), multilayer perceptron with two hidden layers (MLP2) and radial basis function (RBF) network.
The best MLP1 (RMSE = 0.3665) had a 5-2-1 structure (the number of neurons in the input, hidden and output layers, respectively), whereas the most effective MLP2 (RMSE = 0.3679) had a 5-3-6-1 structure.
The best MLP1 (RMSE = 0.1663) and MLP2 (RMSE = 0.1646) had the 8-1-1 and 8-1-47-1 structures, respectively, whereas the structure of the most effective RBF network (RMSE = 0.1719) was 5-4-1.
The models MLP2 and MLP1 have the same objective function and similar constraints.
To test the performance of the models, plenty of instances generated randomly are calculated using models of MNLP, MLP1, and MLP2. The MNLP model is solved using LINGO with a global solver, and the MLP1 and MLP2 models are solved using CPLEX 12.61.
Then, MLP1 and MLP2 models are handled using CPLEX 12.61.
We report the results of Bayesian learning MLPs with one output neuron and one hidden layer with up to three neurons (MLP1, MLP2, MLP3).
The main goal of this study was to establish the algorithm with the best predictive capability among classification and regression trees (CART), chi-square automatic interaction detector (CHAID), radial basis function (RBF) networks and multilayer perceptrons with one (MLP1) and two (MLP2) hidden layers in body weight (BW) prediction from selected body measurements in the indigenous Beetal goat of Pakistan.
radial basis function (RBF) networks and multilayer perceptrons (MLP) with one (MLP1) and two (MLP2) hidden layers can be utilized.
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