back-error propagation neural networks
A comparative study of effort estimation techniques using back propagation algorithm
... Back propagation, "backward propagation of errors", is a common method of training artificial neural networks used in conjunction with an optimization method such as gradient ...
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Estimating of Scour in Downstream of the Water Level Regulation Structures
... Artificial neural networks (ANNs) with learning algorithm of error back propagation (BP) were used to estimate maximum water scour depth, and the model which has seven neurons in its ...
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ECG Signal Compression using Improvised Error Back Propagation Neural Network with GDAL
... In this type of neural network information flow is unidirectional. This network has no feedback (loops) i.e. the output of one layer has no effect on other layer. Feed-forward ANNs tend to be straight forward ...
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Mill Load Control System Based on the Improved Ant Colony Neural Network
... of neural networks were trained using gradient descent algorithm such back propagation algorithm (BP) with a longer convergence time and more probability to fall into local ...the ...
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Sentiment Analysis of Social Media Text Data using Back Propagation in Artificial Neural Networks
... It can be concluded from the mathematical background and subsequent results that the proposed system yields high accuracy for text mining data. The data used here are in the form of tweets. The training algorithm used is ...
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Application of Artificial Neural Network and Genetic Algorithm for Predicting three Important Parameters in Bakery Industries
... work, neural networks with error back-propagation learning algorithms were applied for predicting three important farinograph properties of dough (water absorption, dough development ...
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A FACE RECOGNITION SYSTEM BASED ON PRINCIPAL COMPONENT ANALYSIS USING BACK PROPAGATION NEURAL NETWORKS
... Back propagation is a kind of supervised learning employed by the neural Networks in which the derivative of the error function is propagated back to the contributing neurons in ...
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DEVELOPMENT OF GENETIC ALGORITHM BASED HYBRID NEURAL NETWORK MODEL FOR PREDICTING THE ULTIMATE FLEXURAL STRENGTH OF FERROCEMENT ELEMENTS
... Artificial Neural Networks are highly non-linear and can capture complex interactions among input/output variables in a system without any prior knowledge about the nature of these ...of neural ...
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Review on Financial Forecasting Using Neural Network and Data Mining Technique
... Hopfield network is the most typical feedback network model, it is one of the models which are most commonly studied now. The Hopfield network is the monolayer constituted by the same neuron, and is also a symmetrically ...
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A Robust Intrusion Detection System by Utilizing Support Vector Machine and Error Back Propagation Neural Network
... In this work solving the computer networking problem to give the specific way to prevent the security problems intrusion detection system (IPS) has a main techniques to secure the computer networks problems. Many ...
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Second Order Learning Algorithm for Back Propagation Neural Networks
... The proposed approach was validated on a standard feedforward neural network with one hidden layer by having five hidden nodes. The training data set was created by using the function y = x + sin( 2 * π * x ), ...
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A REAL TIME QRS DETECTION SYSTEM USED ERROR BACK PROPAGATION NEURAL NETWORK
... fast Error Back Propagation Neural Network (EBP-NN) algorithm for QRS complex locations is ...a Back propagation neural ...of back propagation neural ...
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Image Reconstruction Using Super Resolution and Back-propagation Neural Networks
... In this paper, we have studied the relationship between low resolution image (patch) and corresponding high resolution image (patch) .We proposed that the non-linear mapping between LR-image and HR-image can be ...
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1. Breast cancer diagonsis using artifical neural network
... perceptron neural network (MLPNN), combined neural network (CNN), probabilistic neural network (PNN), recurrent neural network (RNN) and support vector machine ...
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A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEMFelix A. Pyatakovich*, Lubov V. Khlivnenko, Tatyana I. Yakunchenko, Kristina F. Makkonen, Olga V. MevshaDOWNLOAD/VIEW
... As indicated above, the computer modeling of ANN training by back error propagation algorithm and by a combination of gradient and stochastic methods has been carried out with Lazarus software. To ...
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Content Based Message Filtering System for OSN User Walls
... The aim of this work is thus to propose and by experimentation valuate an automatic system, referred to as Filtered Wall (FW), ready to filter unwanted messages from OSN user walls. we tend to exploit Machine Learning ...
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The tuning of error signal for back-propagation algorithms
... To analyze the characteristics of grid computing environments, the security challenges in grid computing, the existing password based authentication scheme, the existing grid security [r] ...
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INVESTIGATING DATA MINING BY ARTIFICIAL NEURAL NETWORK: A CASE OF REAL ESTATE PROPERTY EVALUATION
... After training the neural network with its performance depicted in figure 2, results were analyzed and verified based on various visualizations in MATLAB. The following figure 3 shows the clustering of the prices ...
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UGC Approved Journal | Archive :: iosrjen
... Back propagation algorithm, the common and most widely used algorithm in training artificial neural network learns by calculating an error between desired and actual output and propagate the ...
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Designing and implementing a system for Automatic recognition of Persian letters by Lip-reading using image processing methods
... So, in general, it can be concluded that the accuracy of the classification of the voices by the back propagation artificial neural network is higher than the radial basis function. For better and ...
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