C# Class xpidea.neuro.net.bam.BidirectionalAssociativeMemoryNetwork

Implements the Bidirectional Associative Memory (BAM) network.

The Bidirectional Associative Memory (BAM) network that consists of two layers. An input layer and an output layer. The main difference between backpropagation architecture is that BAM does not stop learning when input values reach the output layer. The learning phase stops when the network becomes stable ; no change between input and output values during two consecutive cycles. The pattern sets for training and running and ouput results can only have two values : 1.1 or -1.1. The BAM is a relatively simple neural network architecture with a content addressable memory. BAM is usefull for pattern recognition or with noisy and corrupted patterns. Bam can also "forget" if there are two many patterns in it. BAM becomes saturated when the number of patterns stored is greater than the minimum of the input layer count and the ouput layer node count. BAM System is created to solve this problem.

Inheritance: xpidea.neuro.net.adaline.AdalineNetwork

Afficher le fichier Open project: AlexCherkasov/Neuro.NET Class Usage Examples

Protected Properties

Свойство	Type	Description
inputLayerNodesCount	int
outputLayerNodesCount	int

Méthodes publiques

Méthode	Description
BidirectionalAssociativeMemoryNetwork ( int aInputNodesCount, int aOutputNodesCount ) : System	Creates BAM network.
BidirectionalAssociativeMemoryNetwork ( string fileName ) : System	Creates the network from a file.
Learn ( ) : void	Overridden.Teaches the network.
Load ( BinaryReader binaryReader ) : void	Overridden.Loads network data from a binary stream.
Run ( ) : void	Overridden.Runs the network.
Save ( BinaryWriter binaryWriter ) : void	Overridden.Stores network data in the binary stream.
SetValues ( int index, double value ) : void	Sets value of the node.
SetValuesFromPattern ( Pattern pattern ) : void	Overridden.Loads the values into the input layer from the pattern.
Train ( PatternsCollection patterns ) : void	Overridden.Trains the network to recognize specific patterns. Employs xpidea.neuro.net.bam.BidirectionalAssociativeMemoryOutputNode.Run and xpidea.neuro.net.bam.BidirectionalAssociativeMemoryOutputNode.Learn to teach the network.
UnLearn ( ) : void	Tells the network to "forget" last learn operation.
value ( int index ) : double	Returns value of the output node specified by index.

Méthodes protégées

Méthode	Description
CreateNetwork ( ) : void	Overridden.Constucts the network topology.
GetInputNodesCount ( ) : int	Overridden.Returns nodes count in the input layer.
GetNetworkType ( ) : NeuralNetworkType	Overridden.Returns xpidea.neuro.net.NeuralNetworkType.nntBAM for the BAM network.
GetNodeError ( ) : double	Overridden.Returns node error.
GetOutPutNodesCount ( ) : int	Overridden.Returns nodes count in output layer.
GetOutputNode ( int index ) : NeuroNode	Overridden.Returns output node by its index.
LoadInputs ( ) : void	Overridden.Loads input values into the input layer.

Method Details

BidirectionalAssociativeMemoryNetwork() public méthode

Creates BAM network.

public BidirectionalAssociativeMemoryNetwork ( int aInputNodesCount, int aOutputNodesCount ) : System
aInputNodesCount	int	Number of nodes in the input layer.
aOutputNodesCount	int	Number of nodes in the output layer.
Résultat	System

BidirectionalAssociativeMemoryNetwork() public méthode

Creates the network from a file.

public BidirectionalAssociativeMemoryNetwork ( string fileName ) : System
fileName	string
Résultat	System

CreateNetwork() protected méthode

Overridden.Constucts the network topology.

protected CreateNetwork ( ) : void
Résultat	void

GetInputNodesCount() protected méthode

Overridden.Returns nodes count in the input layer.

protected GetInputNodesCount ( ) : int
Résultat	int

GetNetworkType() protected méthode

Overridden.Returns xpidea.neuro.net.NeuralNetworkType.nntBAM for the BAM network.

protected GetNetworkType ( ) : NeuralNetworkType
Résultat	NeuralNetworkType

GetNodeError() protected méthode

Overridden.Returns node error.

protected GetNodeError ( ) : double
Résultat	double

GetOutPutNodesCount() protected méthode

Overridden.Returns nodes count in output layer.

protected GetOutPutNodesCount ( ) : int
Résultat	int

GetOutputNode() protected méthode

Overridden.Returns output node by its index.

protected GetOutputNode ( int index ) : NeuroNode
index	int	Output node index.
Résultat	NeuroNode

Learn() public méthode

Overridden.Teaches the network.

public Learn ( ) : void
Résultat	void

Load() public méthode

Overridden.Loads network data from a binary stream.

public Load ( BinaryReader binaryReader ) : void
binaryReader	System.IO.BinaryReader	Binary stream reader.
Résultat	void

LoadInputs() protected méthode

Overridden.Loads input values into the input layer.

protected LoadInputs ( ) : void
Résultat	void

Run() public méthode

Overridden.Runs the network.

public Run ( ) : void
Résultat	void

Save() public méthode

Overridden.Stores network data in the binary stream.

public Save ( BinaryWriter binaryWriter ) : void
binaryWriter	System.IO.BinaryWriter	Binary stream writer.
Résultat	void

SetValues() public méthode

Sets value of the node.

public SetValues ( int index, double value ) : void
index	int	Node index.
value	double	New node value.
Résultat	void

SetValuesFromPattern() public méthode

Overridden.Loads the values into the input layer from the pattern.

public SetValuesFromPattern ( Pattern pattern ) : void
pattern	xpidea.neuro.net.patterns.Pattern	Pattern.
Résultat	void

Train() public méthode

Overridden.Trains the network to recognize specific patterns. Employs xpidea.neuro.net.bam.BidirectionalAssociativeMemoryOutputNode.Run and xpidea.neuro.net.bam.BidirectionalAssociativeMemoryOutputNode.Learn to teach the network.

public Train ( PatternsCollection patterns ) : void
patterns	xpidea.neuro.net.patterns.PatternsCollection	Training patterns.
Résultat	void

UnLearn() public méthode

Tells the network to "forget" last learn operation.

public UnLearn ( ) : void
Résultat	void

value() public méthode

Returns value of the output node specified by index.

public value ( int index ) : double
index	int	Node index.
Résultat	double

Property Details

inputLayerNodesCount protected_oe property

Stores nodes count in input layer.

protected int inputLayerNodesCount
Résultat	int

outputLayerNodesCount protected_oe property

Stores nodes count in output layer.

protected int outputLayerNodesCount
Résultat	int