optimal neural network size
Optimal Palette Generation Using Trained Neural Network Approach (TNN)
... of size 321*480 and load into MATLAB using image acquisition ...the neural network with our feature vector table so that it predicts the most efficient pixel value with the minimum ...in ...
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Minimizing Material Inventory In Ready Mixed Concrete Plants By Applying A Fuzzy Neural Network Approach Management
... the size and number of the reservoirs for storage and feeding of batching plant materials based on the results of the prediction of the optimal order quantity, the batching plant of a construction project ...
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The use of adversaries for optimal neural network training
... The input data is pre-processed by NB to transform each variable into a Gaussian dis- tribution. The batch-size was 100, and NB was trained over 150 epochs using the Broy- den–Fletcher–Goldfarb–Shanno algorithm ...
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Study of Neural Network Size Requirements for Approximating Functions Relevant to HEP
... the network size chosen is significantly larger than needed, computational issues training large DNNs may prevent the learning procedure from arriving at an optimal set weights to produce the desired ...
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Original Article Infrared thermal imaging analysis of the human abdomen based on convolution neural network optimized by a genetic algorithm
... convolutional neural network to construct a convolution neural network model (GA-CNN), which provides the basis of a diagnostic tool for doctors, also saving valuable time for ...the ...
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An Efficient Method for Selecting the Optimal Structure of a Fuzzy Neural Network Architecture
... is valid for any k, then such a function is able to split any sample of arbitrary size, in all pos- sible ways, or it is able to fit any data set with zero error. Later on, the well-known problem of over-fitting ...
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A constraint based genetic algorithm for optimizing neural network architectures for detection of loss of coolant accidents of nuclear power plants
... Nowadays, neural networks with 2-hidden layers trained on nuclear simulation transient datasets have been used as a dominant methodology to detect ...the neural networks critically depend on their ...
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Multiple Fault Detection in a Four Stroke Engine Using Single Sensor System
... the size of the feature matrix will be (20 x 6 x 8) 960×8, ...a neural network increases, the complexity of computation is also seen to ...the network was designed by keeping a number of ...
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A new formula to determine the optimal dataset size for training neural networks
... Neural networks (NN) as an approximators need to go through a series of learning process, consists of different steps such as; building a training set, training the network system, then testing its curve ...
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Neural Network Toolbox for MATLAB - Free Computer, Programming, Mathematics, Technical Books, Lecture Notes and Tutorials
... multilayer network with sigmoid functions, since the gradient can have a very small magnitude; and therefore, cause small changes in the weights and biases, even though the weights and biases are far from their ...
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Application of Artificial Neural Networks for Analysis of Flexible Pavements under Static Loading of Standard Axle
... artificial neural network and Kenlayer program is indicative of the high accuracy of the proposed neural network to determine the critical responses of ...artificial neural ...
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Optimizing locomotive body structures using imperialist competitive algorithm
... Artificial neural network was designed with eight neurons in the input layer (thickness of each sheet) and 4 neurons in the output layer (3 first non-zero natural frequency and body structures ...
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OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK
... Artificial Neural Networks ...Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature ...
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Short Term Load Forecasting With Feed Forward Neural Network Algorithm
... all neural networks is Feed Forward Neural Network (FFNN) ...the network to learn complex relationships between input and output patterns, which would be difficult to model with conventional ...
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Fine Grained Classification of Product Images Based on Convolutional Neural Networks
... With the popularity of the Internet and varieties of terminal equipment, online shopping has become a regular part of people’s lives with the onset of websites such as Amazon, Dangdang, Taobao, and Jingdong. Customers ...
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Deep machine learning provides state of the art performance in image based plant phenotyping
... A similar approach was used for the preparation of shoot fea- ture images. For each source image, we selected cropped images at each manually annotated location, as with the root tips. The shoot images are higher ...
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An Optimal Deep Neural Network Model For Lymph Disease Identification And Classification
... An optimal deep neural network (DNN) model is applied to classify the lymph data utilizing the stacked autoencoders (SA) which is generally used to extract the features from the dataset which is ...
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Formulation and Comparison of Experimental Based Mathematical Model with Artificial Neural Network Simulation Model for Optimal Performance of Cotton Pre-Cleaning Machine
... Artificial neural network (ANN). The output of this network can be evaluated by comparing it with observed data and the data calculated from the mathematical model (Paulraj, ...the network is ...
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Improved the Prediction of Multiple Linear Regression Model Performance Using the Hybrid Approach: A Case Study of Chlorophyll a at the Offshore Kuala Terengganu, Terengganu
... Artificial Neural Network to the MLR coins as Artificial Neural Network- Multiple Linear Regression ...Artificial Neural Network (ANN) and proposed hybrid Artificial ...
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Cholesky ANN models for predicting multivariate realized volatility
... the network may lead to overfitted out-of-sample forecasts and the number of connection weights may significantly ...the network, the parameters have been trained with 300 different starting ...the ...
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