error back-propagation training
DISEASE DIAGNOSIS OF HEART MUSCLES USING ERROR BACK PROPAGATION NEURAL NETWORK
... The death caused by heart diseases has become a serious problem, how to diagnose heart diseases efficiently plays a more important role recently. This study presents a comparative study of the classification, accuracy of ...
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Estimating of Scour in Downstream of the Water Level Regulation Structures
... non-uniform propagation resulted in decreasing of ...by error back propagation training algorithm for estimating scour depth (s) and the model which has seven neurons in its hidden ...
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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
... algorithm back propagation, and of that trained by combining a back propagation algorithm and a variant of Cauchy stochastic training, in detecting the degree of activity of an ...
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Bandwidth Analysis by Introducing Slots in Microstrip Antenna Design Using ANN
... input-output training patterns and 50 inputs-output test patterns to validate the ...the training process the neural network automatically adjusts its weights and threshold values such that the error ...
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1. Evaluate the performance of power energy output forecasting in photovoltaic cell
... ANN training is performed with error Back Propagation algorithm and feed forward network is used as network ...various error measurement criteria and performance is ...
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Real Time Face Recognition Using Step Error Tolerance BPN
... (Learning): Back propagation Neural Networks are generally used in classification ...design. Training time which increases substantially with increasing population size is not a disadvantage for our ...
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Implementation of Neural Network Back Propagation Training Algorithm on FPGA
... The Figure 2 shows the architecture of 2:2:2:1 neural network selected to implement basic digital gates i.e, AND, OR, NAND, NOR, XOR, XNOR function. The network has two inputs x1 and x2 with output y. In order to select ...
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ANN Approach for Error Optimization in Process Synchronization Using Back Propagation Algorithm
... of error in the neural network. The innovative idea of Back-propagation algorithm was invented by Werbos Parker et ...The Back-propagation learning algorithm in multi- layer perceptron ...
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A Hybrid Differential Evolution and Back Propagation Algorithm for Feedforward Neural Network Training
... An artificial neural network (ANN) is a well-known computational model which maps input patterns from measurement space into pre- defined classes in the decision space. The feedforward neural net- works (FNN) are widely ...
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Sentiment Analysis of Social Media Text Data using Back Propagation in Artificial Neural Networks
... The training algorithm used is Levenberg-Marquardt (LM) which yields stability in error ...for training and the rest of the 30% have been used for ...to training and testing data sets has been ...
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A REAL TIME QRS DETECTION SYSTEM USED ERROR BACK PROPAGATION NEURAL NETWORK
... The data set is divided into three distinct set is used by neural network to learn patterns present in the data. The testing set is used to evaluate the generalization ability of a supposedly trained network. A final ...
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Adaptive Neuro-Fuzzy Approach To Web-Based Enterprise Software Evaluation
... Uyo. Back-propagation and hybrid learning methods were deployed in training the network comprising software quality attributes of functionality, reliability, usability, efficiency, maintainability, ...
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Artificial Neural Network Modelling of Traffic Noise in Agra Firozabad Highway
... squared error (MSE) for training and cross validation. Thus a simple back propagation of the recurrent network using the momentum training algorithm is proved meaningful supplement for ...
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Artificial Neural Network Involved in the Action of Optimum Mixed Refrigerant (Domestic Refrigerator) (TECHNICAL NOTE)
... 2. Back Propagation Algorithm The BPA uses the steepest-descent method to reach a global ...the training set is presented in the input layer of the network and the error at the output layer is ...
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A Survey On Backpropagation Algorithms For Feedforward Neural Networks
... -The Back-propagation (BP) training algorithm is a renowned representative of all iterative gradient descent algorithms used for supervised learning in neural ...square error (MSE) between the ...
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Optical Character Recognition using Neural Network
... This section describes the basics of artificial neural network and different training algorithms of multi layered feed forward neural network. Fig. 3 shows the basic topology of neural network which has an input ...
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Agricultural Crop Yield Prediction using Deep Learning Approach
... “back propagation” type of neural network states how this type of neural network is ...trained. Back propagation is a type of supervised learning ...the back propagation ...
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Assessment of Accuracy Enhancement of Back Propagation Algorithm by Training the Model using Deep Learning
... There is also reconstruction phase in the RBM in which outputs from the hidden layers are passed to the visible layer with the same set of weights as done in feed forward phase. The weighted sums through the hidden layer ...
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A Robust Intrusion Detection System by Utilizing Support Vector Machine and Error Back Propagation Neural Network
... Intrusion Detection System (IDS) is an example of exploitation Detection System that works for detecting malicious attacks. This can be described as software for security management. Many researchers have proposed the ...
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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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