Examples of org.encog.neural.pattern.ElmanPattern

org.encog.neural.pattern.ElmanPattern
This class is used to generate an Elman style recurrent neural network. This network type consists of three regular layers, an input output and hidden layer. There is also a context layer which accepts output from the hidden layer and outputs back to the hidden layer. This makes it a recurrent neural network. The Elman neural network is useful for temporal input data. The specified activation function will be used on all layers. The Elman neural network is similar to the Jordan neural network. @author jheaton

 */
public class ElmanXOR {


  static BasicNetwork createElmanNetwork() {
    // construct an Elman type network
    ElmanPattern pattern = new ElmanPattern();
    pattern.setActivationFunction(new ActivationSigmoid());
    pattern.setInputNeurons(1);
    pattern.addHiddenLayer(6);
    pattern.setOutputNeurons(1);
    return (BasicNetwork)pattern.generate();
  }

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public class TestSRN  extends TestCase {
  
  public void performElmanTest(int input, int hidden, int ideal)
  {
    // we are really just making sure no array out of bounds errors occur
    ElmanPattern elmanPattern = new ElmanPattern();
    elmanPattern.setInputNeurons(input);
    elmanPattern.addHiddenLayer(hidden);
    elmanPattern.setOutputNeurons(ideal);
    BasicNetwork network = (BasicNetwork)elmanPattern.generate();
    MLDataSet training = RandomTrainingFactory.generate(1000, 5, network.getInputCount(), network.getOutputCount(), -1, 1);
    ResilientPropagation prop = new ResilientPropagation(network,training);
    prop.iteration();
    prop.iteration();    
  }

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  private static MLMethod createElman() {
    CreateElmanDialog dialog = new CreateElmanDialog(EncogWorkBench
        .getInstance().getMainWindow());
    if (dialog.process()) {
      ElmanPattern elman = new ElmanPattern();
      elman.setInputNeurons(dialog.getInputCount().getValue());
      elman.addHiddenLayer(dialog.getHiddenCount().getValue());
      elman.setOutputNeurons(dialog.getOutputCount().getValue());
      elman.setActivationFunction(new ActivationTANH());
      return elman.generate();
    } else
      return null;


  }

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public class TestSRN  extends TestCase {
  
  public void performElmanTest(int input, int hidden, int ideal)
  {
    // we are really just making sure no array out of bounds errors occur
    ElmanPattern elmanPattern = new ElmanPattern();
    elmanPattern.setInputNeurons(input);
    elmanPattern.addHiddenLayer(hidden);
    elmanPattern.setOutputNeurons(ideal);
    BasicNetwork network = (BasicNetwork)elmanPattern.generate();
    MLDataSet training = RandomTrainingFactory.generate(1000, 5, network.getInputCount(), network.getOutputCount(), -1, 1);
    ResilientPropagation prop = new ResilientPropagation(network,training);
    prop.iteration();
    prop.iteration();    
  }

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Related Classes of org.encog.neural.pattern.ElmanPattern

org.encog.examples.neural.recurrent.elman.ElmanXOR

org.encog.neural.networks.BasicNetwork

org.encog.neural.networks.layers.BasicLayer

org.encog.neural.networks.TestSRN

org.encog.workbench.process.CreateNeuralNetwork

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