backpropagation artificial neural networks
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Polar backpropagation [artificial neural networks]
... The purpose of this paper is to present a modified backpropagation architecture which can solve the polar classification problem and locate the pole a t the same [r] ...
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EFFICIENT COMPARISON BASED SELF DIAGNOSIS USING BACKPROPAGATION ARTIFICIAL NEURAL NETWORKS
... Copyright to IJIRSET www.ijirset.com 1547 (AIS) can be used for fault diagnosis in large multiprocessor systems containing several hundred nodes. In diagnosable systems each fault set can be uniquely identified by one of ...
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Convergence Optimization of Backpropagation Artificial Neural Network Used for Dichotomous Classification of Intrusion Detection Dataset
... Artificial neural networks (ANN) are in general considered as an effective approach of handling and classifying patterns. However, soft precision, long training cycles as well as overstimulation ...
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Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning
... The default method for improving generalization is the so-called early stopping. This technique is automatically provided for all of the supervised network creation functions in the Neural Networks toolbox, ...
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Date Fruits Classification using MLP and RBF Neural Networks
... using artificial neural networks ...of neural networks have been applied as classifiers: multi-layer perceptron (MLP) with backpropagation and radial basis function RBF ...the ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... Artificial Neural Networks (ANNs) are biologically inspired methods modeled on the learning processes of human ...brain. Artificial Neural Networks (ANNs) works by processing ...
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A Comparison of Machine Learning Classifiers Applied to Financial Datasets
... Three types of algorithms were used to have a better perspective in the domain of the present analysis: Naïve Bayes learning, Artificial Neural Networks (Backpropagation) and Decision Trees ...
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Study on Experiments of Artificial Neural Network Using Spatial Data
... the Artificial Neural Network (ANN) to satellite image ...of neural networks using the backpropagation network, and effect of changing ANN parameters on the accuracy of image ...
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A NOVEL LEADER ELECTION ALGORITHM FOR HONEYCOMB MESH NETWORKS
... various Artificial Neural Network (ANN) algorithms, such as the Variable Learning Rate Backpropagation model, Bayesian Regularization model, Levenburg-Marquardt model and others, to compare and ...
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Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data
... The Artificial neural network on the other hand has been determined to be an effective tool in classification though the operations within the network structure are ...as neural networks, ...
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Analysis of Backpropagation Algorithm in Predicting the Most Number of Internet Users in the World
... in artificial neural networks that uses supervised learning that is popular and has advantages in its learning abilities ...The backpropagation algorithm is used for training. The ...
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Training Artificial Neural Networks: Backpropagation via Nonlinear Optimization
... Backpropagation through time variants was in- troduced after 10 iterations. The iterations of steepest descent variants require less cpu time than the iterations of conjugate gradients ver- sions, but the ...
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Temperature-Based Feed-Forward Backpropagation Artificial Neural Network For Estimating Reference Crop Evapotranspiration In The Upper West Region
... the networks have to make sense of the inputs without outside ...of networks utilize supervised training. Neural networks have been trained to perform complex function in various fields of ...
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INTERNETWORKING INDONESIA JOURNAL
... Artificial Neural Network (ANN) or Network Neural (ANN) is one method that has been known for prediction ...of neural networks to remember and make generalizations from what already ...
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Attempting to Mimic the Brain Synthetically
... behind neural networks. A neural network is composed of “a set of simple computing units which influence each other through modifiable connections, or ...These artificial networks were ...
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On the application and design of artificial neural networks for motor fault detection. II.
... P of the use of artificial neural networks in motor fault detection applications. In Part I1 of this paper, we will discuss how to design an artificial neural network for[r] ...
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1. Diagnosing alzheimer’s disease and mild cognitive impairment with modalities: a survey
... Convolutional Neural Networks (CNN) is used to generate features that can classify AD from MCI and from HC giving an average of 80% correct classification by using a Multi layered Feedforward Perceptron ...
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Detecting Network Intrusions Using Signal Processing with Query-Based Sampling Filter
... Pulse code modulation (PCM) is a digital technique that involves sampling an analog signal at regular intervals and coding the measured amplitude into a series of binary values, which are transmitted by modulation of a ...
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Research status and applications of nature-inspired algorithms for agri-food production
... The development of ANNs started designing the artificial neuron to mimic the characteristics of the biological neuron. The human nervous system is built of cells called neurons. Figure 1 shows the structure of a ...
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Prediction of Seismic zone in India using Neural Network Algorithms
... deep neural network optimizers such as Stochastic Gradient Descent (SGD), Adaptive Gradient algorithm (AdaGrad) and Root Mean Square propagation (RMSprop) are used to optimize the prediction model and find the ...
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