neural network in java source code

backpropagation artificial neural network in c++ - codeproject

... The binary floating point file format is expedient when you have a large amount of data. The data is saved in aseparate file as a sequence of floating point numbers in binary format, using 4 ... backprop training are optional. You may use them for validationand testing of your network, for input data normalization, and error limits during training process.>ann1dn t network. nn data1_file ... layer:ANNetwork::ANNetwork(const wchar_t *fname);ANNetwork::ANNetwork(int layers_number, int *neurons_per_layer);int nerons_per_layer[4] = {128, 64, 32, 10};ANNetwork *ann = new ANNetwork(4,...

Tài liệu Programming Neural Networks in JavaProgramming Neural Networks in Java will show the intermediate ppt

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... every other neuron in a Hopfield Neural Network. A Hopfield Neural Network can be trained to recognize certain patterns. Training a Hopfield Neural Network involves performing some basic matrix ... particularly sure what final outcome is being sought. Neural networks are often employed in data mining do to the ability for neural networks to be trained. Neural networks can also be used ... propagation refers to the way in which the neurons are trained in this sort of neural network. Chapter 3 begins your introduction into this sort of network. A Fixed Wing Neural Network Some researchers...

application of back-propagation neural network in data forec

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Báo cáo hóa học: "Investigating the synchronization of hippocampal neural network in response to acute nicotine exposure" pdf

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Hybrid energy system evaluation in water supply system energy production: neural network approach

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Tài liệu Neural Network Applications in Intelligent doc

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... leadscrew grinding processusing neural networks, Computers in Industry, 23, 169, 1993. 86. Chen, J. S., Neural network- based modeling and error compensation of thermally-induced spindleerrors, International ... theuse of neural networks is still constrained to simulations on sequential computing machines. Traininga large network using a sequential machine can be time-consuming. Fortunately, training usually ... types of neural networks included ART networks, Hopﬁeld networks, and SOM neural networks. Weaknesses of neural networks for modeling and design of manufacturing systems result from neural networks...

neural network for beginners (part 1 of 3) - codeproject

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... multi-layer neural networks. As this article is mainly geared towards dealing with singlelayer networks, let's dicuss those further:Single layer neural networksSingle-layer neural networks ... all of the input training vectors is called an epoch) of the training set has occured withouterror, training is complete.At this time any input training vector may be presented to the network ... acheivable using single layer (perceptron) networks. The next part of this article series will showhow to do this using muti-layer neural networks, using the back propogation training method.Well...

ai _ neural network for beginners (part 2 of 3) - codeproject

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... be :NN_Trainer_XOR : Trains a Neural Network to solve the XOR problemTrainerEventArgs : Training event args, for use with a GUINeuralNetwork : A configurable Neural Network NeuralNetworkEventArgs ... accurate)TRAINING RESULTS TabViewing the training phase target/outputs togetherViewing the training phase errorsIt is also possible to view the Neural Networks final configuration using the "View ... and"Supervised"Reinforcement In Reinforcement learning, during training, a set of inputs is presented to the Neural Network, the Output is 0.75,when the target was expecting 1.0.The error (1.0 - 0.75) is used for training...

ai_ neural network for beginners (part 3 of 3) - codeproject

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... training it.LearningThere are essentially two types of learning that may be applied to a neural network, which are "Reinforcement" and"Supervised".Reinforcement In Reinforcement ... Reinforcement learning, during training, a set of inputs is presented to the neural network. The output is 0.75 whenthe target was expecting 1.0. The error (1.0 - 0.75) is used for training ("wrong ... entire training loop is done and there is still no well-configured neural network, I simply return thevalue of the winner (of the last training epoch) as the overall best configured neural network. This...

an introduction to encog neural networks for java - codeproject

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... combined to create the training data for the XOR operator. The following line of code combines these two arrays to create training data:NeuralDataSet trainingSet = new BasicNeuralDataSet(XOR_INPUT, ... 'generalpurpose' trainer for this type of neural network is called resilient propagation (RPROP). The following code creates anRPROP trainer:final Train train = new ResilientPropagation (network, trainingSet);Once ... » Neural NetworksAn Introduction to Encog Neural Networks for Java By JeffHeaton, 17 Jan 2010Download source code - 306 KBIntroductionThis article provides a basic introduction to neural networks...

cneuralnetwork make your neural network learn faster - codeproject

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... ann_train _network_ from_file wherelearning rate update is performed once per epoch.int CNeuralNetwork::ann_train _network_ from_file (char *file_name, int max_epoch, float max_error, int parsing_direction){ ... Weight initialization method = NGUYEN.Target of minimum mean square error is achieved within 172 epochs.Points of InterestAll the code is implemented in a single class: CNeuralNetwork. In that ... new neural network. void ann_create _network( unsigned int input_num, unsigned int output_num, unsigned int hidden_layer_num, );Set learning rate value.void ann_set_learning_rate(float learning_rate...

comparing neural networks in neuroph, encog and joone - codeproject

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... takeconsiderably more training iterations.We begin by creating a training set.TrainingSet trainingSet = new TrainingSet(2, 1);trainingSet.addElement(new SupervisedTrainingElement (new double[]{0, ... trained network s results. System.out.println(" ;Neural Network Results:"); for(TrainingElement element : trainingSet.trainingElements()) { network. setInput(element.getInput()); network. calculate(); ... DynamicBackPropagation();train.setNeuralNetwork (network) ; network. setLearningRule(train);We now begin to loop through training iterations, until we are trained to below 1%.int epoch = 1;do{ train.doOneLearningIteration(trainingSet);...

convolutional neural network workbench - codeproject

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... loading and providing thenecessary MNIST and CIFAR-10 training and testing samples. There is also a NeuralNetworkDataSet class that is used by the project to load andsave neural network definitions, ... InitializeDefaultNeuralNetwork() function in MainViewWindows.xaml.cs):NeuralNetworks network = new NeuralNetworks("LeNet-5", 0.8D, LossFunctions.MeanSquareError, DataProviderSets.MNIST, 0.02D); network. Layers.Add(new ... Training Parameters' dialog where you can define the basictraining parameters. The 'Training Scheme Editor' button gives you the possibility to fully define your own training...