/**
* Copyright 2010 Neuroph Project http://neuroph.sourceforge.net
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.neuroph.nnet;
import org.neuroph.core.Layer;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.input.Difference;
import org.neuroph.core.input.Intensity;
import org.neuroph.core.transfer.Gaussian;
import org.neuroph.nnet.learning.LMS;
import org.neuroph.util.ConnectionFactory;
import org.neuroph.util.LayerFactory;
import org.neuroph.util.NeuralNetworkFactory;
import org.neuroph.util.NeuralNetworkType;
import org.neuroph.util.NeuronProperties;
import org.neuroph.util.TransferFunctionType;
/**
* Radial basis function neural network.
*
* TODO: learning for rbf layer: k-means clustering
*
* @author Zoran Sevarac <sevarac@gmail.com>
*/
public class RbfNetwork extends NeuralNetwork {
/**
* The class fingerprint that is set to indicate serialization
* compatibility with a previous version of the class.
*/
private static final long serialVersionUID = 1L;
/**
* Creates new RbfNetwork with specified number of neurons in input, rbf and output layer
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param rbfNeuronsCount
* number of neurons in rbf layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
public RbfNetwork(int inputNeuronsCount, int rbfNeuronsCount, int outputNeuronsCount) {
this.createNetwork(inputNeuronsCount, rbfNeuronsCount, outputNeuronsCount);
}
/**
* Creates RbfNetwork architecture with specified number of neurons in input
* layer, output layer and transfer function
*
* @param inputNeuronsCount
* number of neurons in input layer
* @param rbfNeuronsCount
* number of neurons in rbf layer
* @param outputNeuronsCount
* number of neurons in output layer
*/
private void createNetwork(int inputNeuronsCount, int rbfNeuronsCount,
int outputNeuronsCount) {
// init neuron settings for this network
NeuronProperties rbfNeuronProperties = new NeuronProperties();
rbfNeuronProperties.setProperty("weightsFunction", Difference.class);
rbfNeuronProperties.setProperty("summingFunction", Intensity.class);
rbfNeuronProperties.setProperty("transferFunction", Gaussian.class);
// set network type code
this.setNetworkType(NeuralNetworkType.RBF_NETWORK);
// create input layer
Layer inputLayer = LayerFactory.createLayer(inputNeuronsCount, TransferFunctionType.LINEAR);
this.addLayer(inputLayer);
// create rbf layer
Layer rbfLayer = LayerFactory.createLayer(rbfNeuronsCount, rbfNeuronProperties);
this.addLayer(rbfLayer);
// create output layer
Layer outputLayer = LayerFactory.createLayer(outputNeuronsCount, TransferFunctionType.LINEAR);
this.addLayer(outputLayer);
// create full conectivity between input and rbf layer
ConnectionFactory.fullConnect(inputLayer, rbfLayer);
// create full conectivity between rbf and output layer
ConnectionFactory.fullConnect(rbfLayer, outputLayer);
// set input and output cells for this network
NeuralNetworkFactory.setDefaultIO(this);
// set appropriate learning rule for this network
this.setLearningRule(new LMS());
}
}