Fast Artificial Neural Network

Summary

Implementation of FANN (Fast Artificial Neural Network) regression to provide ANN (Artificial Neural Network) regression.

Fast Artificial Neural Network Regression

Usage

Navigate to and click on ALS Forest > Regression Analysis > Fast Artificial Neural Network.

Fast Artificial Neural Network Regression

Settings

  • Import Training Data: Refer to Sample Data and Independent Variables.
  • Independent Variables: Refer to Sample Data and Independent Variables.
  • Momentum (default value is "0.6"): Set the momentum parameter in ANN regression analysis for selecting the optimized path.
  • Learning Rate (default value is "0.7"): The global learning rate for training the network.
  • Accuracy Assessment: Based on the K-Fold cross validation model, a sample would be partitioned into k subsets according to input K-Fold value (no less than 2). Take one of the subsets as a validation dataset and the remaining subsets as training datasets to form a model, then run this model and test the fitting of the validation set to training sets. Repeat this process until every subset is treated as a validation set at least once then select out the model with the least MSE (mean square error) as the optimal model.
  • Save Regression Model: Tick the checkbox to save the ANN model (Fast Artificial Neural Network.model) under the output path.
  • Save Regression Dataset: Tick the checkbox to save the ANN training dataset (Fast Artificial Neural Network.csv) in .csv format under the output path.
  • Output Path: Choose an output directory. A fast artificial neural network regression model report (Fast Artificial Neural Network.html), recording the model's parameters and accuracy (R-square, RMSE), would be generated under this directory. A prediction result file (Fast Artificial Neural Network.tif), based on the fast artificial neural network regression model and input variables from a .tif or .csv file, would also be generated under this output directory.
Fast Artificial Neural Network Regression

Note: The dimension of imported sample/training data must be within the scope of independent variables, which may be adjusted accordingly. The model/result is based on the passed-in variables.

results matching ""

    No results matching ""