public class Tool4NeuralNetwork
extends java.lang.Object
Tool4NeuralNetwork
class provides the functionality
of a neural network.
title: SystemCoreLib
description: SystemCoreLib
copyright: (c) 2012 thomas kaffka, born at 11/08/1959 in düsseldorf - germany , all rights reserved.
Modifier and Type | Field and Description |
---|---|
double |
_nAverageError |
double |
_nEpsilon |
int |
_nH |
long |
_nLearnStep |
double |
_nMaxError |
double |
_nMinError |
double |
_nStdDeviationError |
double[][] |
_w1 |
int |
_w1_i |
int |
_w1_j |
double[][] |
_w2 |
int |
_w2_i |
int |
_w2_j |
double[] |
factor |
Constructor and Description |
---|
Tool4NeuralNetwork(java.lang.String content,
int hidden,
double epsilon,
boolean std_deviation)
The constructor
Tool4NeuralNetwork creates a new instance(object) of the class. |
Modifier and Type | Method and Description |
---|---|
double[] |
executeFunction(double[] x)
The method
executeFunction executes the function f(x). |
void |
learning()
The method
learning the learning procedure. |
void |
setValues(int nH,
int count_x,
int count_y,
double[] factor,
double epsilon)
The method
setValues set the values for executing the neural network. |
public double[] factor
public double[][] _w1
public int _w1_i
public int _w1_j
public double[][] _w2
public int _w2_i
public int _w2_j
public int _nH
public double _nEpsilon
public long _nLearnStep
public double _nMaxError
public double _nMinError
public double _nAverageError
public double _nStdDeviationError
public Tool4NeuralNetwork(java.lang.String content, int hidden, double epsilon, boolean std_deviation)
Tool4NeuralNetwork
creates a new instance(object) of the class.content
- the example data.hidden
- size of the hidden layer.epsilon
- the learn parameter epsilon.std_deviation
- thue = calculate the standard deviation.public void setValues(int nH, int count_x, int count_y, double[] factor, double epsilon)
setValues
set the values for executing the neural network.nH
- the hidden layer.count_x
- the number of the x coordinates.count_y
- the number of the y coordinates.factor
- the data factor.epsilon
- the learn parameter epsilon.public double[] executeFunction(double[] x)
executeFunction
executes the function f(x).x
- the arguments of the function.public void learning()
learning
the learning procedure.