backpropagation based neural networks
Training Artificial Neural Networks: Backpropagation via Nonlinear Optimization
... guide backpropagation algorithm used for training artificial neural ...descent-based backpropagation algorithm, and four dif- ferent variants of conjugate gradient algorithm are ...prediction. ...
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Using constraints to improve generalisation and training of feedforward neural networks : constraint based decomposition and complex backpropagation
... In 1943, McCulloch and Pitts published their paper describing the two state threshold neuron [McCulloch, 1943] and showed that any finite logical expression can be implemented by this type of neuron. As more recent ...
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Static Hand Gesture Recognition of Indonesian Sign Language System Based on Backpropagation Neural Networks
... System Based on Backpropagation Neural ...filter based on skin ...of neural network architecture with 4096 neurons in input layer, 75 neurons in hidden layer and 15 neurons in output ...
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Backpropagation neural network as earthquake early warning tool using a new modified elementary Levenberg–Marquardt Algorithm to minimise backpropagation errors
... Artificial neural networks could be used for the EEW; Gentili and Michelini (2006) designed automatic picking of P- and S-wave phases using artificial neural networks for EEW by training 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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Detecting Network Intrusions Using Signal Processing with Query-Based Sampling Filter
... makes neural networks flexible and powerful in ...statistical neural network classifier for anomaly detection is ...different neural network classifiers, the backpropagation ...
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Date Fruits Classification using MLP and RBF Neural Networks
... artificial neural networks (ANN). The classification system are based on attributes extracted from dates fruits obtained from a computer vision system (CVS) ...of neural networks have ...
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Research status and applications of nature-inspired algorithms for agri-food production
... and based on modeling the neural network process models were inversed through numerical optimization to design and implement model predictive controllers to handle the nonlinearity and input-output time ...
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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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EFFICIENT COMPARISON BASED SELF DIAGNOSIS USING BACKPROPAGATION ARTIFICIAL NEURAL NETWORKS
... In this paper, a new diagnosis approach using neural networks is used to solve the fault identification problem using partial syndromes. The problem of efficiently identifying the set of faulty nodes when ...
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INTERNETWORKING INDONESIA JOURNAL
... Artificial Neural Network (ANN) or Network Neural (ANN) is one method that has been known for prediction ...forecast based on the pattern of events in the ...of neural networks to ...
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Performance Analysis of Neural Networks Training using Real Coded Genetic Algorithm
... methods, neural networks and other machine learning ...artificial neural network (ANN) is classification using multilayer perceptron (MLP), which represents a generalization of single-layer ...
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Rainfall Forecasting Using Backpropagation Neural Network
... of Neural Networks are Multilayer Perception (MLP) that being combined with Backpropagation ...City based on inflation rate obtained error rate only ...propagation Neural Network ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... feed-forward neural network (MLFFNN) consists of an input layer, hidden layer and an output layer of ...forward neural network consists of one or more layers of usually non- linear processing units (can use ...
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Daily Network Traffic Prediction Based on Backpropagation Neural Network
... Since 2011, Universitas Mulawarman (UNMUL) has a network traffic that connects the ICT Center and all the faculties, institutes, and units with a bandwidth capacity of 150 Mbps. In 2012, there are more web-based ...
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Open Journal Systems
... with backpropagation learning algorithm and with two hidden layers each consisting 4 and 3 neurones is well fit with rainfall prediction as it obtain minimum MAE compared to the other two ...
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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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Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast Cancer Diagnosis
... A training method for feedforward ANN by using a multiobjective genetic algorithm (MOGA) [22]. The study used a noisy data and then found that the MOGA can reduce the ANN size and error rates as well. In other work, a ...
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A subjective job scheduler based on a backpropagation neural network
... scheduler based on a Backpropagation Neural Network (BPNN) and a greedy job alignment ...is based on the similarity measure of the jobs with the seen dataset of the ...is based on the ...
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Prediction of Salinity Variations in a Tidal Estuary Using Artificial Neural Network and Three Dimensional Hydrodynamic Models
... about the best-fit line. The performance of the BPNN and RBFNN was better than that of the three-dimensional hydrodynamic model during the training and verification phases, as revealed by the values of the RMSE and MAE . ...
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