artificial neural networks training
Artificial neural networks training acceleration through network science strategies
... In the Coil20 dataset (Fig. 2b), a short transient phase with no evident improvements among the simulations with differ- ent values of ζ emerges. Indeed, there are just small accuracy fluctuations of ±3%. These results ...
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Artificial Neural Networks Approach in Microwave Filter Tuning
... In order to check to what extent the presented approach is effective, the process of tuning of both RX and TX filter of diplexer is performed. The ANN architecture is the same as in the previous consideration, 512 input ...
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Prediction on Flexural Strength of Over-Reinforced Concrete Beams Using Artificial Neural Networks
... forward neural network with a feed forward back propagation algorithm was adopted which was shown in ...well-known training algorithms for the multilayer perceptron, is a gradient descent technique to ...
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NUCLEAR REACTOR CORE PRESSURE LOSS ADJUSTMENT WITH NEURAL APPROACH
... To investigate loss of pressure control on nuclear reactor design models artificial neural networks supports plant operators by training with process parameter database pertaining to accident ...
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Prediction of Compressive Strength of Concrete using Artificial Neural Network
... following: training data = 60%, validation data = 20% and testing data = 20%, the number of hidden layers was 1, 2 and 3, the number of hidden neurons was 17 in each layer decided based on the experience and trial ...
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The effect of data pre processing on optimized training of artificial neural networks
... Data pre-processing is an important step in the data mining process. Mostly, data gathering methods are lightly controlled, resulting in outliers, impossible data combinations, and missing values, etc. Analyzing data ...
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Algorithms for Optimized Training of Artificial Neural Networks
... 26 BP without momentum is a first order minimization which utilizes gradient information to follow a path of steepest descent. The inclusion of momentum term in BP algorithm overshoots the local minima problem as it ...
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Analysis of cardiovascular (cvd)/coronary heart diseases(chd) using artificial neural network (ann)
... the neural network refers to a network of biological neurons. Artificial neural network (ANN) is the mimicking of the human neuron on a ...neurons.[1][2]. Artificial neural ...
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A. Artificial Neural Networks
... enhance artificial neural networks methods to support the prediction in stock market ...a neural network model for technical analysis of stock market, and its application to a buying and ...
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Short and long-term forecasting using artificial neural networks for stock prices in Palestine: a comparative study
... When choosing models, it is common to use a portion of the available data for testing, and use the rest of the data for estimating (or “training”) the model. Then the testing data can be used to measure how well ...
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A Comprehensive Review on McCulloch Pitts Neuron Model
... The Artificial Neural networks resembles the brain of a human in Two different aspects ...An Artificial Neural Network is characterised by its Architecture, Training and ...
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Navigation with Artificial Neural Networks
... This type of combination may make training an ANN more di ffi cult. In this type of correction, the filter state at the next time step is a function of the ANN estimate. The simple approach to train the state ...
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Introduction to the Artificial Neural Networks
... of training examples. In the second step we need to gather a training data set that satisfactory describe a given ...gathered training data set in form understandable to a chosen artificial ...
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A Study on Effective Algorithm for Medical Decision Making System
... or artificial neural networks, which are part of artificial ...A neural network has the ability to mimic this type of decision-making process, and use a knowledge base of information, ...
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Cotton genotypes selection through artificial neural networks.
... RNA training acquired information from 300 genotypes per group with the same properties (average vector and variance-covariance matrix) of original ...- Training and validation; 2014/15 season - Prediction ...
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Artificial Neural Network Architectures: Work Point Count System Coupled With Back-Propagation And Resilient Back - Propagation Algorithm For Solving Double Dummy Bridge Problem In Contract Bridge
... of Artificial Neural Networks (ANN) is applied by training on sample deals and used to estimate the number of tricks to be taken by one pair of bridge players is the key idea behind Double ...
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Point Count Systems in Imperfect Information Game
... the artificial neural networks which were used to estimate the number tricks to be taken by one pair of players in the double dummy bridge problem in contract ...cascade-correlation neural ...
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Short term load forecasting based on hybrid artificial neural networks and particle swarm optimisation
... Traditional BP is a gradient descent local search procedure that measures the output error, calculates the gradient of the error by adjusting the weights in the descending gradient direction whereas SCG is a second order ...
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Determination of Corrosion Types from Electrochemical Noise by Artificial Neural Networks
... from training data and recognized different corrosion types from a series of input (feature vectors containing many parameters) and output (different corrosion types) without any prior assumptions about their ...
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Title: CLASSIFICATION ON BREAST CANCER USING GENETIC ALGORITHM TRAINED NEURAL NETWORK
... The neural system is arranged into hidden layers, input and output of the Artificial Neural Networks ...or training stage, the ability of the ANN to generalize the problem with samples ...
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