neural network training process
BAT ALGORITHM FOR ROUGH SET ATTRIBUTE REDUCTION
... Grey neural network model is denoted by GNNM(h, n), wherein, h is the order of the differential equations, n is the number of sequences involved in the ...Grey neural network model lets a grey ...
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A Comparison Between GA and PSO Algorithms in Training ANN to Predict the Refractive Index of Binary Liquid Solutions
... Artificial neural network algorithm (ANN) The applicability of ANN in process industries and scientific research has been growing contin- uously in recent years ...the network architecture. ...
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A Review: Evaluating the Parametric Optimization of Electrical Discharge Machining (EDM) by Using & Comparing Artificial Neural Network (ANN) and Genetic Algorithm (GA)
... Multiperceptron neural network models were developed using Neuro Solutions ...the network is optimized with ...(EDM) process using soft computing techniques. Artificial neural ...
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Sentiment Analysis Using Hybrid Machine Learning Technique
... Neural network has been applied in this study. The concept of Artificial neural network(ANN) is similar to the working of human brain which learns to take decision from the past experience ...
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A Predictive Model for Corrosion Inhibition of Mild Steel by Thiophene and Its Derivatives Using Artificial Neural Network
... inhibition process by means of structurally selected common variables; that is to use QSAR information for internal mechanistic predictions among training molecules to see their inter-relation respecting ...
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Verification and validation of neural networks: a sampling of research in progress
... a neural network to determine whether a dynamic neural network has adjusted ...reasoning process of the ...to training class norms and, should the new value fall well outside the ...
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Optimizing Process Parameters of Rotary Furnace using Bio fuels: An Interactive ANN Approach
... artificial neural networks have been used extensively as a soft computing tool because of its inherent capability to learn mapping between a set of input and output values ...the network is trained for a ...
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Architecture and training algorithm of feed forward articial neural network to predict material removal rate of electrical discharge machining process
... the training starts. The training continues until the iterations reach the target level of ...the network is evaluated by the mean sum of the squared error (MSE) between the measured and predicted ...
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Improving the accuracy of Iris Recognition System using Neural Network and Particle swarm Optimization
... Propagation Neural Network (FFBNN) for training purpose. While training the parameters of the FFBNN are optimized using ...extraction process is given to a well-trained FFBNN-PSO to ...
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Architecture of the Extended-Input Binary Neural Network and Applications
... artificial neural network is inspired by the structure and function of the major parts of the ...forward neural networks and have fixed weights to transform input patterns into a simple code and then ...
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Identification Of Weeds From Crops Using Convolutional Neural Network
... convolutional neural networks (CNNs) connected to the input RGB plus Near Infra-Red (NIR) ...labeling process of the images collection in the ...for training out of which it separates into nine ...
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Research of Teacher’s Performance Evaluation Model Based on AHP and Improved PSO BP Neural Network
... hierarchy process; literature [2] constructs comprehensive evaluation index system from four aspects of comprehensive quality, teaching work, social work and personnel training, and directly stipulate the ...
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Rule Extraction and Insertion to Improve the Performance of a Dynamic Cell Structure Neural Network
... the neural network on a large data set to determine if there are things that the expert may have ...initial neural network structure, and then the data is presented to the neural ...
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Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning
... making process has always been an issue in diagnos- tic decision ...course neural network ...the training set is not simpler than the backpropagation method used here for ...
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A novel wind turbine gearbox fault diagnosis method based on ASO-VMD and NRF
... the training process, the initial error of the neural network is ...the training set achieves better ...the neural network realized the gradient decline in the previous ...
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Performance Improvement of Convolutional Neural Network for Pulmonary Nodule Detection
... Convolutional Neural Network(CNN) in Lung CAD ...Convolutional Neural Network to overcome this ...at Training process, we obtained ...
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A SHORT-TERM TRAFFIC FLOW FORECASTING METHOD BASED ON STATE IDENTIFICATION.
... State identification:The three parameters of traffic flow, speed and density will be used to determine the status, each parameter will produce a result of the state, and the results of the state value does not ...
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A New Recommender System for Hashtags
... for training the naive bayes classifier is ...of training examples and ‘d’ is the dimensionality of the features which is the number of words in a tweet (maximum 140 words due the character ...in ...
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Neural Network Based Rainfall Prediction
... artificial neural network. To train neural network we provide multiple districts’ rainfall data and a desired overall ...the neural network to predict a list that matches the ...
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Feature selection for robust Detection of Distributed Denial-of-Service attacks using genetic algorithms
... robust neural network detector for Distributed Denial-of-Service (DDoS) attacks in computers providing Internet ...error-free neural network DDoS detector using only 14 ...
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