gradient descent neural network algorithm
Mill Load Control System Based on the Improved Ant Colony Neural Network
... Artificial neural network can approximate nonlinear continuous function with arbitrary precision and avoid the complex behavior for the solved problem in the modeling ...addition, neural ...
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Noise Induced Hearing Loss (NIHL) prediction in humans using a modified back propagation neural network
... on network training time, an efficient Back Propagation and Acceleration Learning Method (BPALM) was introduced to reduce the training time of conventional ...momentum Algorithm (PBPAM), where the momentum ...
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Analysis Of Line To Ground Fault In Transformer By Elman’S Network Using Gradient Descent Back Propagation Algorithm
... The gradient algorithm is based on conjugate directions, as in traincgp, traincgf, and traincgb, but this algorithm does not perform a line search at each ...
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Neural Network Training By Gradient Descent Algorithms: Application on the Solar Cell
... artificial neural network trained at every time, separately, by one algorithm among the optimization algorithms of gradient descent (Levenberg-Marquardt, Gauss-Newton, Quasi-Newton, ...
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The Effect of Adaptive Gain and Adaptive Momentum in Improving Training Time of Gradient Descent Back Propagation Algorithm on Classification Problems
... propagation algorithm based on extrapolation of each individual interconnection ...each network weight in back propagation algorithm is individually ...conjugate gradient algorithm ...
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Noise-Induced Hearing Loss (NIHL) Prediction in Humans Using a Modified Back Propagation Neural Network
... Propagation Neural Network has been used widely in the practical fields and has a strong capability of classifying problems, but it has problems of slow convergence and network stagnancy, which still ...
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The effect of adaptive gain and adaptive momentum in improving training time of Gradient Descent back propagation algorithm on classification problems
... propagation algorithm based on extrapolation of each individual interconnection ...each network weight in back propagation algorithm is individually ...conjugate gradient algorithm ...
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A Neural Network MLSE Receiver Based on Natural Gradient Descent: Application to Satellite Communications
... Figures 5 and 6 show that the di ff erent parts of the channel have been successfully identified: the linear filter (Figure 5), the TWT AM/AM conversion (Figure 6a), and the TWT AM/PM conversion (Figure 6b). Note that, ...
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Algorithmic Guarantees for Inverse Imaging with Untrained Neural Network Priors
... projected gradient descent (PGD) algorithm for solving the problem of compressive sensing with a deep untrained network ...deep neural network priors for compressive sensing 1 , ...
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Calibrated Stochastic Gradient Descent for Convolutional Neural Networks
... stochastic gradient descent (SGD) and its variants, the op- timized gradient estimators may be as expensive to compute as the true gradient in many ...stochastic gradient descent ...
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An Improved Gauss Newtons Method based Back propagation Algorithm for Fast Convergence
... steepest descent back-propagation (SDBP) is used in several applications despite its asymptotic slow convergence rate ...The algorithm is also known as a gradient ...steepest descent ...
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Optimal Parameter Selection Using Three-term Back Propagation Algorithm for Data Classification
... (BP) algorithm is the most popular supervised learning method for multi-layered feed forward Neural ...uses Gradient Descent (GD) method to learn the ...learning algorithm have been ...
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Hybrid Optimized Back propagation Learning Algorithm for Multi layer Perceptron
... the network system, most common one among those practices is use gradient descent method in back propagation learning to optimize weight vector and minimize error ...this gradient ...
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Comparison of Neural Network Training Algorithms for Classification of Heart Diseases
... Artificial neural network (ANN) technique can be used to predict or classification patients getting a heart ...eight neural network training algorithms for classification of heart disease data ...
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Modelling of direct metal laser sintering of EOS DM20 bronze using neural networks and genetic algorithms
... the neural network were normalized in the scale of 0 to 1. In neural network toolbox of MATLAB, feed-forward networks were developed using 12 different training algorithms, namely traingd ...
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A FASTER ESTIMATION ALGORITHM APPLIED TO POWER QUALITY PROBLEMS
... and neural network ...of neural network is that it requires significant ...of Neural Network by using Particle Swarm Optimization (PSO) combined with Gradient ...
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Facial Expression Recognition System using Neural Network based on LBP Features
... the neural network are run and during every trial, the results of the neural network are found to be drastically different most of the ...the neural network proceeds, the ...
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Machine Learning from a Continuous Viewpoint. Weinan E. Princeton University
... We will see that gradient descent algorithm (GD) for random feature and neural network models are simply the particle method discretization of the gradient flows discussed before..?. “co[r] ...
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Performance Evaluation of Neural Network based Cognitive eNodeB in LTE Uplink
... random neural network (RNN) with gradient descent (GD) and Levenberg Marquardt (LM) training algorithm based framework is used to improve inter cell interference coordination (ICIC) and ...
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Performance Evaluation Analysis of MLP & DG-RBF Feed Forward Neural Networks for Pattern Classification of Handwritten English Curve Scripts Naveen Kumar Sharma 1, S R Pande2 and Manu Pratap Singh 3*
... forward neural network trained with back propagation algorithm does not perform better in comparison to feed forward neural network trained with decent gradient with ...DG-RBF ...
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