Examples of org.encog.neural.prune.PruneIncremental

org.encog.neural.prune.PruneIncremental

This class is used to help determine the optimal configuration for the hidden layers of a neural network. It can accept a pattern, which specifies the type of neural network to create, and a list of the maximum and minimum hidden layer neurons. It will then attempt to train the neural network at all configurations and see which hidden neuron counts work the best. This method does not simply choose the network with the lowest error rate. A specifiable number of best networks are kept, which represent the networks with the lowest error rates. From this collection of networks, the best network is defined to be the one with the fewest number of connections. Not all starting random weights are created equal. Because of this, an option is provided to allow you to choose how many attempts you want the process to make, with different weights. All random weights are created using the default Nguyen-Widrow method normally used by Encog.

    this.iterations = iterations;
    this.training = training;
    this.pattern = pattern;
    this.windowSize = windowSize;
    
    this.prune = new PruneIncremental(this.training, this.pattern,
        this.iterations, this.weightTries , this.windowSize, this);
    this.prune.init();


    this.buttonStart = new JButton("Start");
    this.buttonStop = new JButton("Stop");

Examples of org.encog.neural.prune.PruneIncremental

Related Classes of org.encog.neural.prune.PruneIncremental