Class Summary |
AbstractEventNotifier |
This class raises an event notification invoking the corrisponnding
Monitor.fireXXX method. |
AbstractLearner |
This class provides some basic simple functionality that can be used (extended) by other learners. |
BasicLearner |
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BatchLearner |
BatchLearner stores the weight/bias changes during the batch and updates them
after the batch is done. |
BiasedLinearLayer |
This layer consists of linear neurons, i.e. |
BufferedSynapse |
This class implements a synapse that permits to have asynchronous
methods to write output patterns. |
CircularSpatialMap |
This class implements the SpatialMap interface providing a circular spatial map for use with the GaussianLayer and Kohonen Networks. |
ContextLayer |
The context layer is similar to the linear layer except that
it has an auto-recurrent connection between its output and input. |
DelayLayer |
Delay unit to create temporal windows from time series
O---> Yk(t-N)
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... |
DelayLayerBeanInfo |
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DelaySynapse |
This Synapse connects the N input neurons with the M output neurons
using a matrix of FIRFilter elements of size NxM. |
DirectSynapse |
This is forward-only synapse. |
EKFFFNLearnerPlugin |
A plugin listener that implements the EKFFFN learner used
to train feed forward neural networks. |
EKFRNNLearnerPlugin |
A plugin listener that implements the EKF learner, based on
"Some observations on the use of the extended Kalman filter
as a recurrent network learning algorithm" by Williams (1992)
in order to train a network. |
ExtendableLearner |
Learners that extend this class are forced to implement certain functions, a
so-called skeleton. |
ExtendedKalmanFilterFFN |
Implements the extended Kalman filter (EKF) as described in
"Using an extended Kalman filter learning algorithm for feed-forward
neural networks to describe tracer correlations" by Lary and Mussa (2004)
in order to train a feed-forward neural network. |
ExtendedKalmanFilterRNN |
Implements the extended Kalman filter (EKF) as described in
"Some observations on the use of the extended Kalman filter
as a recurrent network learning algorithm" by Williams (1992)
in order to train a recurrent neural network. |
Fifo |
The Fifo class represents a first-in-first-out
(FIFO) stack of objects. |
FIRFilter |
Element of a connection representing a FIR filter (Finite Impulse Response). |
FreudRuleFullSynapse |
Deprecated. possible bug in implementation |
FullSynapse |
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GaussianLayer |
This layer implements the Gaussian Neighborhood SOM strategy. |
GaussianLayerBeanInfo |
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GaussianSpatialMap |
This class implements the SpatialMap interface providing a circular spatial map for use with the GaussianLayer and Kohonen Networks. |
GaussLayer |
The output of a Gauss(ian) layer neuron is the sum of the weighted input values,
applied to a gaussian curve (exp(- x * x) ). |
KohonenSynapse |
This is an unsupervised Kohonen Synapse which is a Self Organising Map. |
KohonenSynapseBeanInfo |
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Layer |
The Layer object is the basic element forming the neural net. |
LayerBeanInfo |
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LinearLayer |
The output of a linear layer neuron is the sum of the weighted input values,
scaled by the beta parameter. |
LinearLayerBeanInfo |
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LogarithmicLayer |
This layer implements a logarithmic transfer function. |
Matrix |
The Matrix object represents the connection matrix of the weights of a synapse
or the biases of a layer. |
MatrixBeanInfo |
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MemoryLayer |
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MemoryLayerBeanInfo |
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Monitor |
The Monitor object is the controller of the behavior of the neural net. |
MonitorBeanInfo |
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NetErrorManager |
This class should be used when ever a critical error occurs that would impact on the training or running of the network. |
NetStoppedEventNotifier |
Raises the netStopped event from within a separate Thread |
NeuralNetAdapter |
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NeuralNetEvent |
Transport class used to notify the events raised from a neural network |
OutputSwitchSynapse |
This class acts as a switch that can connect its input to one of its connected
output synapses. |
OutputSwitchSynapseBeanInfo |
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Pattern |
The pattern object contains the data that must be processed from a neural net. |
PatternBeanInfo |
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RbfGaussianLayer |
This class implements the nonlinear layer in Radial Basis Function (RBF)
networks using Gaussian functions. |
RbfGaussianParameters |
This class defines the parameters, like center, sigma, etc. |
RbfInputSynapse |
The synapse to the input of a radial basis function layer should't provide a
single value to every neuron in the output (RBF) layer, as is usual the case. |
RbfLayer |
This is the basis (helper) for radial basis function layers. |
RpropLearner |
This class implements the RPROP learning algorithm. |
RpropParameters |
This object holds the global parameters for the RPROP learning
algorithm (RpropLearner). |
RTRL |
A RTRL implementation. |
RTRLLearnerFactory |
A RTRL implementation. |
RTRLLearnerPlugin |
A plugin listener that applies the RTRL algorithm to a network. |
SangerSynapse |
This is the synapse useful to extract the principal components
from an input data set. |
SigmoidLayer |
The output of a sigmoid layer neuron is the sum of the weighted input values,
applied to a sigmoid function. |
SimpleLayer |
This abstract class represents layers that are composed
by neurons that implement some transfer function. |
SimpleLayerBeanInfo |
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SineLayer |
The output of a sine layer neuron is the sum of the weighted input values,
applied to a sine (sin(x) ). |
SoftmaxLayer |
The outputs of the Softmax layer must be interpreted as probabilities. |
SpatialMap |
SpatialMap is intended to be an abstract spatial map for use with a
GaussianLayer. |
Synapse |
The Synapse is the connection element between two Layer objects. |
SynapseBeanInfo |
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TanhLayer |
Layer that applies the tangent hyperbolic transfer function
to its input patterns |
TanhLayerBeanInfo |
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WTALayer |
This layer implements the Winner Takes All SOM strategy. |
WTALayerBeanInfo |
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