Variables used to train the neural network
The prediction of vibration and noise for the high-speed train based on neural network and boundary element method
... high-speed train based on nonlinear auto-associative neural network (NARX NN) and multi-body dynamics model is ...is used to compute the low frequency noise of the high-speed ...
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Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system
... each train per trip, that can help KTMB to plan for its future ...Artificial Neural Network were carried out by using Alyuda Neurointelligence ...
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Biological Neural Network Structure and Spike Activity Prediction Based on Multi Neuron Spike Train Data
... micro-scale neural network structure for the brain is essential for the investigation on the brain and ...the neural network structure through brain slicing and reconstruction via nanoscale ...
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Comparison of Neural Network Based Controllers for Nonlinear EMS Magnetic Levitation Train
... Maglev train based on NARMA-L2, model reference and predictive ...Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input ...
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Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering
... Abstract Tracking user reported bugs requires consider- able engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural ar- chitecture that ...
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Comparison of Neural Network NARMA-L2 Model Reference and Predictive Controllers for Nonlinear EMS Magnetic Levitation Train
... Maglev train based on NARMA-L2, model reference and predictive ...Maglev train with the proposed controllers for the precise role of a Magnetic levitation machine have been as compared for a step input ...
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Semi empirical models for drying of agricultural products by used structured artificial neural network
... It can happen that we have a priori information about the structure of the system (to be modelled), but don’t have the data to train the subsequent ANNs that make up the semi- empirical model as a combination of ...
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TRAIN AND ANALYZE NEURAL NETWORKS TO FIT YOUR DATA
... the network, namely, the ...the neural network, are discussed, then this compact form becomes more convenient to use than an explicit one, such as that of Equation ...of neural networks from ...
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Performance Comparison of Binarized Neural Network with Convolutional Neural Network
... The comparison showed that it is possible to train Binarized Neural Network on MNIST, CIFAR10 and achieve near state-of-the-art results. The effect on the performance in BNN by adding dropout layer ...
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A Nonlinear Autoregressive Model with Exogenous Variables Neural Network for Stock Market Timing: The Candlestick Technical Analysis
... NARX neural network for prediction while first the data preparation and then pre-processing are done before the data is manipulated to train the network and to ...stage, network is ...
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Application of Neural Network with Error Correlation and Time Evolution for Retrieval of Soil Moisture and Other Vegetation Variables
... Propagation Neural Network with Error Correlation that incorporates a novel rule, error correlation learning, to train a feed forward neural network [1, 7, ...
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Prediction of the influent wastewater variables using neural network theory
... 36: Network performance showing the linear regression (correlation) coefficients for training data (normalized) on the left, and the validation data set (denormalized) on the right for t[r] ...
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How To Train A Neural Net
... An important question is how do we get the pseudo data. In some domains large amounts of unlabeled data is easy to collect (e.g. in text, web and image domains) and can be used as pseudo data. In other domains, ...
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Neural Network Approach for Solving Singular Convex Optimization with Bounded Variables
... bounded variables constraint rather than the common unconstraint ...novel neural network model was proposed for solving the problem of singular convex optimization with bounded ...proposed ...
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Neural Network Used For Translating the Speech to Devanagari Text Using MATLAB
... IX. CONCLUSION This paper describe the implementation of automatic speech recognition system which can be easily synthesized and recovered from neural network classifier and MFCC is a very reliable tool for ...
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A neural network-based methodology of quantifying the association between the design variables and the users' performances
... In the study of display-control compatibility, the users may be required to exercise translatory or circular motion in operating a control device rather than simply pushing or pulling a control. Direction-of-motion ...
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Early Detection of Bacteraemia Using Ten Clinical Variables with an Artificial Neural Network Approach
... LRP, Layer-wise Relevance Propagation; OOS, one-out search. ANN, artificial neural network; MLP, multi-layer perceptron; RF, random forest; and SVM, support vector machine. 4. Discussion The classic models ...
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FPGA Based Design of Artificial Neural Processor Used for Wireless Sensor Network
... Sensor Network WSN, and then presenting proper ...Back-propagation Neural Network BPNN has been used as the proposed intelligent system for this work, whereas the BPNN is considered as a ...
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Neural and Computational Representations of Decision Variables
... Beyond goal-value signals, we also found evidence for value independent category identity codes within a region of central OFC, but also extending more medially to overlap with some of the value coding areas. These ...
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Reliable Train Network with Active Supervisor
... 4. Simulation Outcomes In [24], the simulations presented the outcomes for the unique states which the network experiences. The same approach is used in this research. However, after analyz- ing the unique ...
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