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 a separate file as a sequence of floating point numbers in binary format, using 4 ... backprop training are optional. You may use them for validation and 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,...
Ngày tải lên: 28/04/2014, 10:10
Tài liệu Programming Neural Networks in JavaProgramming Neural Networks in Java will show the intermediate ppt
... 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...
Ngày tải lên: 14/02/2014, 20:20
application of back-propagation neural network in data forec
Ngày tải lên: 28/04/2014, 10:18
Báo cáo hóa học: "Investigating the synchronization of hippocampal neural network in response to acute nicotine exposure" pdf
Ngày tải lên: 19/06/2014, 08:20
Hybrid energy system evaluation in water supply system energy production: neural network approach
Ngày tải lên: 05/09/2013, 14:58
Tài liệu Neural Network Applications in Intelligent doc
... leadscrew grinding process using neural networks, Computers in Industry, 23, 169, 1993. 86. Chen, J. S., Neural network- based modeling and error compensation of thermally-induced spindle errors, International ... the use of neural networks is still constrained to simulations on sequential computing machines. Training a 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...
Ngày tải lên: 17/12/2013, 06:15
neural network for beginners (part 1 of 3) - codeproject
... multi-layer neural networks. As this article is mainly geared towards dealing with single layer networks, let's dicuss those further: Single layer neural networks Single-layer neural networks ... all of the input training vectors is called an epoch) of the training set has occured without error, 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 show how to do this using muti-layer neural networks, using the back propogation training method. Well...
Ngày tải lên: 28/04/2014, 10:10
ai _ neural network for beginners (part 2 of 3) - codeproject
... be : NN_Trainer_XOR : Trains a Neural Network to solve the XOR problem TrainerEventArgs : Training event args, for use with a GUI NeuralNetwork : A configurable Neural Network NeuralNetworkEventArgs ... accurate) TRAINING RESULTS Tab Viewing the training phase target/outputs together Viewing the training phase errors It 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...
Ngày tải lên: 28/04/2014, 10:10
ai_ neural network for beginners (part 3 of 3) - codeproject
... training it. Learning There 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 when the 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 the value of the winner (of the last training epoch) as the overall best configured neural network. This...
Ngày tải lên: 28/04/2014, 10:10
an introduction to encog neural networks for java - codeproject
... 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, ... 'general purpose' trainer for this type of neural network is called resilient propagation (RPROP). The following code creates an RPROP trainer: final Train train = new ResilientPropagation (network, trainingSet); Once ... » Neural Networks An Introduction to Encog Neural Networks for Java By JeffHeaton, 17 Jan 2010 Download source code - 306 KB Introduction This article provides a basic introduction to neural networks...
Ngày tải lên: 28/04/2014, 10:10
cneuralnetwork make your neural network learn faster - codeproject
... ann_train _network_ from_file where learning 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 Interest All 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...
Ngày tải lên: 28/04/2014, 10:10
comparing neural networks in neuroph, encog and joone - codeproject
... take considerably 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); ...
Ngày tải lên: 28/04/2014, 10:10
convolutional neural network workbench - codeproject
... loading and providing the necessary MNIST and CIFAR-10 training and testing samples. There is also a NeuralNetworkDataSet class that is used by the project to load and save 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 basic training parameters. The 'Training Scheme Editor' button gives you the possibility to fully define your own training...
Ngày tải lên: 28/04/2014, 10:10
computer programming - java - neural network gui with joone (2002)
Ngày tải lên: 29/04/2014, 14:52
Báo cáo hóa học: " Research Article Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction" pdf
Ngày tải lên: 21/06/2014, 20:20
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