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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 |
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| ExtendableLearner |
Learners that extend this class are forced to implement certain functions, a
so-called skeleton. |
| 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 |
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| 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 |
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| 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). |
| 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)). |
| 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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