Name | Description | Type | Package | Framework |
Adaline | Adaline neural network architecture with LMS learning rule. | Class | org.neuroph.nnet | Neuroph |
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AntiHebbianLearning | A variant of Hebbian learning called Anti-Hebbian learning. | Class | org.neuroph.nnet.learning | Neuroph |
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AutoencoderNetwork | | Class | org.neuroph.nnet | Neuroph |
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BackPropagation | Back Propagation learning rule for Multi Layer Perceptron neural networks. | Class | org.neuroph.nnet.learning | Neuroph |
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BAM | Bidirectional Associative MemoryAuthor:Zoran Sevarac See Also:Serialized Form | Class | org.neuroph.nnet | Neuroph |
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BiasNeuron | Neuron with constant high output (1), used as biasAuthor:Zoran Sevarac See Also:Neuron, | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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BinaryDeltaRule | Delta rule learning algorithm for perceptrons with step functions. | Class | org.neuroph.nnet.learning | Neuroph |
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BinaryHebbianLearning | Hebbian-like learning algorithm used for Hopfield network. | Class | org.neuroph.nnet.learning | Neuroph |
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Cluster | This class represents a single cluster, with corresponding centroid and assigned vectorsAuthor:Zoran Sevarac | Class | org.neuroph.nnet.learning.kmeans | Neuroph |
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CompetitiveLayer | Represents layer of competitive neurons, and provides methods for competition. | Class | org.neuroph.nnet.comp.layer | Neuroph |
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CompetitiveLearning | Competitive learning rule. | Class | org.neuroph.nnet.learning | Neuroph |
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CompetitiveNetwork | Two layer neural network with competitive learning rule. | Class | org.neuroph.nnet | Neuroph |
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CompetitiveNeuron | Provides neuron behaviour specific for competitive neurons which are used in competitive layers, and networks with competitive learning. | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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ConvolutionalBackpropagation | | Class | org.neuroph.nnet.learning | Neuroph |
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ConvolutionalLayer | Convolutional layer is a special type of layer, used in convolutional neural networks. | Class | org.neuroph.nnet.comp.layer | Neuroph |
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ConvolutionalNetwork | Convolutional neural network with backpropagation algorithm modified for convolutional networks. | Class | org.neuroph.nnet | Neuroph |
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ConvolutionalUtils | Utility functions for convolutional networksAuthor:Boris Fulurija, Zorn Sevarac | Class | org.neuroph.nnet.comp | Neuroph |
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DelayedConnection | Represents the connection between neurons which can delay signal. | Class | org.neuroph.nnet.comp | Neuroph |
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DelayedNeuron | Provides behaviour for neurons with delayed output. | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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DynamicBackPropagation | Backpropagation learning rule with dynamic learning rate and momentumAuthor:Zoran Sevarac See Also:Serialized Form | Class | org.neuroph.nnet.learning | Neuroph |
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ElmanNetwork | Under development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized Form | Class | org.neuroph.nnet | Neuroph |
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FeatureMapsLayer | This class represents an array of feature maps which are 2 dimensional layers (Layer2D instances) and it is base class for Convolution and Pooling layers, | Class | org.neuroph.nnet.comp.layer | Neuroph |
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GeneralizedHebbianLearning | A variant of Hebbian learning called Generalized Hebbian learning. | Class | org.neuroph.nnet.learning | Neuroph |
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Hopfield | Hopfield neural network. | Class | org.neuroph.nnet | Neuroph |
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HopfieldLearning | Learning algorithm for the Hopfield neural network. | Class | org.neuroph.nnet.learning | Neuroph |
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InputLayer | Represents a layer of input neurons - a typical neural network input layerAuthor:Zoran Sevarac See Also:InputNeuron, | Class | org.neuroph.nnet.comp.layer | Neuroph |
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InputMapsLayer | Input layer for convolutional networksAuthor:Boris Fulurija, Zoran SevaracSee Also:Serialized Form | Class | org.neuroph.nnet.comp.layer | Neuroph |
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InputNeuron | Provides input neuron behaviour - neuron with input extranaly set, which just transfer that input to output without change. | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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InputOutputNeuron | Provides behaviour specific for neurons which act as input and the output neurons within the same layer. | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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Instar | Instar neural network with Instar learning rule. | Class | org.neuroph.nnet | Neuroph |
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InstarLearning | Hebbian-like learning rule for Instar network. | Class | org.neuroph.nnet.learning | Neuroph |
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JordanNetwork | Under development: Learning rule BackProp Through Time requiredAuthor:zoranSee Also:Serialized Form | Class | org.neuroph.nnet | Neuroph |
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Kernel | Kernel used in convolution networks. | Class | org.neuroph.nnet.comp | Neuroph |
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KMeansClustering | 1. | Class | org.neuroph.nnet.learning.kmeans | Neuroph |
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KNearestNeighbour | calculate distances to all vectors from list and find minimum vector | Class | org.neuroph.nnet.learning.knn | Neuroph |
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Kohonen | Kohonen neural network. | Class | org.neuroph.nnet | Neuroph |
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KohonenLearning | Learning algorithm for Kohonen network. | Class | org.neuroph.nnet.learning | Neuroph |
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KVector | Represents feature vector used in k-means clustering algorithmAuthor:Zoran Sevarac, Uros Stojkic | Class | org.neuroph.nnet.learning.kmeans | Neuroph |
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Layer2D | 2D Layer provides 2D layout of the neurons in layer. | Class | org.neuroph.nnet.comp.layer | Neuroph |
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Layer2D .Dimensions | Dimensions (width and height) of the Layer2DSee Also:Serialized Form | Class | org.neuroph.nnet.comp.layer | Neuroph |
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LMS | LMS learning rule for neural networks. | Class | org.neuroph.nnet.learning | Neuroph |
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MaxNet | Max Net neural network with competitive learning rule. | Class | org.neuroph.nnet | Neuroph |
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MomentumBackpropagation | Backpropagation learning rule with momentum. | Class | org.neuroph.nnet.learning | Neuroph |
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MultiLayerPerceptron | Multi Layer Perceptron neural network with Back propagation learning algorithm. | Class | org.neuroph.nnet | Neuroph |
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NeuroFuzzyPerceptron | The NeuroFuzzyReasoner class represents Neuro Fuzzy Reasoner architecture. | Class | org.neuroph.nnet | Neuroph |
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OjaLearning | Oja learning rule wich is a modification of unsupervised hebbian learning. | Class | org.neuroph.nnet.learning | Neuroph |
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Outstar | Outstar neural network with Outstar learning rule. | Class | org.neuroph.nnet | Neuroph |
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OutstarLearning | Hebbian-like learning rule for Outstar network. | Class | org.neuroph.nnet.learning | Neuroph |
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Perceptron | Perceptron neural network with some LMS based learning algorithm. | Class | org.neuroph.nnet | Neuroph |
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PerceptronLearning | Perceptron learning rule for perceptron neural networks. | Class | org.neuroph.nnet.learning | Neuroph |
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PoolingLayer | Pooling layer is a special type of feature maps layer (FeatureMapsLayer) which is used in convolutional networks. | Class | org.neuroph.nnet.comp.layer | Neuroph |
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RBFLearning | Learning rule for Radial Basis Function networks. | Class | org.neuroph.nnet.learning | Neuroph |
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RBFNetwork | Radial basis function neural network. | Class | org.neuroph.nnet | Neuroph |
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ResilientPropagation | Resilient Propagation learning rule used for Multi Layer Perceptron neural networks. | Class | org.neuroph.nnet.learning | Neuroph |
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SigmoidDeltaRule | Delta rule learning algorithm for perceptrons with sigmoid (or any other diferentiable continuous) functions. | Class | org.neuroph.nnet.learning | Neuroph |
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SimulatedAnnealingLearning | This class implements a simulated annealing learning rule for supervised neural networks. | Class | org.neuroph.nnet.learning | Neuroph |
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SupervisedHebbianLearning | Supervised hebbian learning rule. | Class | org.neuroph.nnet.learning | Neuroph |
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SupervisedHebbianNetwork | Hebbian neural network with supervised Hebbian learning algorithm. | Class | org.neuroph.nnet | Neuroph |
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ThresholdNeuron | Provides behaviour for neurons with threshold. | Class | org.neuroph.nnet.comp.neuron | Neuroph |
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UnsupervisedHebbianLearning | Unsupervised hebbian learning rule. | Class | org.neuroph.nnet.learning | Neuroph |
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UnsupervisedHebbianNetwork | Hebbian neural network with unsupervised Hebbian learning algorithm. | Class | org.neuroph.nnet | Neuroph |