A B C D G H I M O R S T W X

A

adjustWeight() - Method in class Synapse

Adjusts the weight

B

BiasNeuron - class BiasNeuron.

The BiasNeuron class creates Bias Neurons which have a fixed value of -1

BiasNeuron() - Constructor for class BiasNeuron

This constructor for the BiasNeuroninitialises it's input value to -1

C

ContextNeuron - class ContextNeuron.

The ContextNeuron class creates Context Neuron which are used to provide recurrency in the RNN network.

ContextNeuron(double) - Constructor for class ContextNeuron

This constructor for the ContextNeuron specifies the memory depth and sets the input value to 0.5

ContextSynapse - class ContextSynapse.

The ContextSynapse class is used to connect Context Neurons to Hidden Neurons.

ContextSynapse(Neuron, Neuron) - Constructor for class ContextSynapse

This constructor for the ContextSynapse connects two Neurons together, and sets it's weight to 1

calculateDelayedOutput() - Method in class HiddenNeuron

Calculates the summation of the delayed output values

calculateDelayedOutput() - Method in class InputNeuron

Calculates the summation of the delayed output values

calculateHiddenError() - Method in class TDNN

Calculates the hidden error term

calculateHiddenOutput() - Method in class TDNN

Calculates the output of the Hidden Neurons

calculateHiddenWeightChange() - Method in class TDNN

Calculates the weight change to be made to the Synapses between the Hidden and Input Neurons and Bias Neurons

calculateOutput() - Method in class BiasNeuron

Calculates the output value

calculateOutput() - Method in class ContextNeuron

Calculates the output value

calculateOutput() - Method in class InputNeuron

Calculates the output value

calculateOutputWeightChange() - Method in class TDNN

Calculates the weight change to be made to the Synapses between the Output and Hidden Neurons and Bias Neurons

calculateWeightChange(double, double, double) - Method in class Synapse

Calculates the weight change

calculateWeightChange(double, double, double, int) - Method in class Synapse

Calculates the average weight change for use with time delays

closeFile() - Method in class OutputFile

Closes the file

connectNeurons() - Method in class RNN

Connects the Neurons

createNeurons(double) - Method in class RNN

Creates the Neurons

createNeurons(int) - Method in class TDNN

Creates the Neurons

D

DataProcessor - class DataProcessor.

The DataProcessor class is used to scale the input data between 0 and 1

DataProcessor(double[]) - Constructor for class DataProcessor

This constructor for the DataProcessor sets it's inputs

G

getData(String, String) - Method in class Test

Gets the data from the Document

getDocument() - Method in class XMLParser

Gets the Document

getOutputs() - Method in class HiddenNeuron

gets the delayed output values

getOutputs() - Method in class InputNeuron

gets the delayed output values

getWeight() - Method in class Synapse

Gets the Synapse weight

H

HiddenNeuron - class HiddenNeuron.

The HiddenNeuron class is used to create Hidden Neurons

HiddenNeuron(int, int) - Constructor for class HiddenNeuron

This constructor for the HiddenNeuron sets the slope of it's activation function and the number of it's inputs

HiddenNeuron(int, int, int) - Constructor for class HiddenNeuron

This constructor for the HiddenNeuron sets the slope of it's activation function, the number of it's inputs and the number of delays

I

InputNeuron - class InputNeuron.

InputNeuron class is used to create Input Neurons

InputNeuron(int) - Constructor for class InputNeuron

This constructor for the InputNeuron sets the slope of it's activation function

InputNeuron(int, int) - Constructor for class InputNeuron

This constructor for the InputNeuron sets the slope of it's activation function and the number of delays

initialise() - Method in class MLP

Initialises the network with data

initialise() - Method in class RNN

Initialises the network with data

initialise() - Method in class TDNN

Initialises the network with data

input(double) - Method in class ContextNeuron

Set the new input value and adds the old value to the memory

input(double) - Method in class InputNeuron

Sets the new input Value

M

MLP - class MLP.

The MLP class is used to create Multilayer Perceptron networks

MLP(int, int, int, double, double, int, String) - Constructor for class MLP

This constructor for the MLP specifies the number of Input Neurons and Hidden Neurons the slope of their activation functions, the learning rate, the momentum, the number of training epochs and set a network topology id for use with log files.

main(String[]) - Static method in class Test

O

OutputFile - class OutputFile.

The OutputFile class creates log files

OutputFile(String) - Constructor for class OutputFile

Constructor for the OutputFile

OutputNeuron - class OutputNeuron.

The OutputNeuron class creates Output Neurons

OutputNeuron(int, int) - Constructor for class OutputNeuron

This constructor for the OutputNeuron sets the slope of it's activation function and the number of it's inputs

R

RNN - class RNN.

The RNN class is based on the Elman Network, which has recurrency at the Hidden Layer.

RNN(int, int, double, int, double, double, int, String) - Constructor for class RNN

This constructor for the RNN specifies the number of Input Neurons and Hidden Neurons, the memory depth, the slope of their activation functions, the learning rate, the momentum, the number of training, epochs and set a network topology id for use with log files.

S

Synapse - class Synapse.

The Synapse class is used to connect Neurons together.

Synapse(Neuron, Neuron) - Constructor for class Synapse

This constructor for the Synapse connects two Neurons together, and sets it's weight to a random value

scale() - Method in class DataProcessor

Scales it's inputs array between 0 and 1

sendToHidden() - Method in class RNN

Transfers the weighted values to the Hidden Neurons

sendToOutput() - Method in class RNN

Transfers the weighted values to the Output Neuron

setWeight(int) - Method in class Synapse

Sets the Synapse weight

T

TDNN - class TDNN.

The TDNN class is used to create Time Delay Neural Networks

TDNN(int, int, int, int, double, double, int, String) - Constructor for class TDNN

This constructor for the TDNN specifies the number of Input Neurons and Hidden Neurons, the number of delays, the slope of their activation functions, the learning rate, the momentum, the number of training, epochs and set a network topology id for use with log files.

Test - class Test.

The Test class is the main class.

Test(String, String, String, String, String, String) - Constructor for class Test

This constructor for the Test creates training, testing and validating data sets

testMLP() - Method in class Test

Trains, tests and validates the MLP networks

testRNN() - Method in class Test

Trains, tests and validates the RNN networks

testTDNN() - Method in class Test

Trains, tests and validates the TDNN networks

transferValue() - Method in class ContextSynapse

Transfers a weighted value from the input Neuron to the output Neuron

transferValue() - Method in class Synapse

Transfers a weighted value from the input Neuron to the output Neuron

W

writeToFile(String) - Method in class OutputFile

Writes data to the file

X

XMLParser - class XMLParser.

The XMLParser class is used to parse XML documents and create W3C DOM objects

XMLParser(String) - Constructor for class XMLParser

This constructor for the XMLParserbuilds a new Document from an XML file

A B C D G H I M O R S T W X