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Artificial Neural Network Model

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment

A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment

... (PCA). Artificial Neural Networks (ANNs) are used to model the relationship between low resolution (LR) and high resolution (HR) information drawn from hazard ...first model, there is one ANN ...

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Development of an Artificial Neural Network (ANN) Model for Estimating Cemented Paste Backfill Performance

Development of an Artificial Neural Network (ANN) Model for Estimating Cemented Paste Backfill Performance

... For example, Fall et al (2007) applied the response surface methods based modeling to predict the technical and economical performance properti es of the CPB and analysis[r] ...

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Local-scale valley wind retrieval using an artificial neural network applied to routine weather observations

Local-scale valley wind retrieval using an artificial neural network applied to routine weather observations

... model, it has been verified that this difference cannot be attributed to a shadowing effect on the observations. In the morning, the boundary layer becomes unstable 3 h earlier in simulations than in ...

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Bridging FEM and Artificial Neural Network in gating system design for smart 3D sand casting

Bridging FEM and Artificial Neural Network in gating system design for smart 3D sand casting

... is applied at the metal/sand mold interface [35]. Fig. 3. CAD and FE mesh applied on the studied model 4.2. Gating velocity and S/N ratio’s calculation The evolution of the ingate velocity (i.e. velocity measured ...

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Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning

... both model architectures performed better with shorter sequences (table ...additional artificial regular grammars from a recent review 62 and tested both our recurrent and feedforward architectures on each ...

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An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean

An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean

... Many studies have focused on the causes and consequences of eutrophication in coastal waters (e.g., Kemp et al. 1997). However, none to our knowledge has adopted a comparative approach to address the factors influencing ...

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Artificial Neural Network Models for Alternative Investments

Artificial Neural Network Models for Alternative Investments

... returns. The Jarque-Bera, Anderson-Darling and Liliefors tests highly reject the Gaussian hypothesis, whilst the Kolmogorov-Smirnov test overall result is more contrasted. The two former tests mostly evaluate differences ...

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Modeling the creep compliance of asphalt concrete using the artificial neural network technique

Modeling the creep compliance of asphalt concrete using the artificial neural network technique

... INTRODUCTION The creep compliance was adopted in the mechanistic–empirical pavement design guide (MEPDG) developed under the NCHRP project 1-37A. It is mainly used to describe the behavior of asphalt concrete at low ...

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Flux prediction using artificial neural network (ANN) for the upper part of glycolysis

Flux prediction using artificial neural network (ANN) for the upper part of glycolysis

... The artificial neural network could be used to predict the product outcome ...regression model- ling methods, multiple linear regression (MLR), principal component regression (PCR) and partial ...

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Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies

Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies

... tations model the behavior and the interaction of dozens of reactors, fuel cycle facilities, mass flows; and timescales can introduce strong non-linearities with the presence of many threshold ...

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Design of a thermoacoustic sensor for low intensity ultrasound measurements based on an artificial neural network

Design of a thermoacoustic sensor for low intensity ultrasound measurements based on an artificial neural network

... principle can be used to calibrate a range of transducers. Different from spatial-peak temporal-average intensity (I spta ), which represents the maximum intensity in the acoustic field, spatial-average temporal-average ...

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Weight Identification Through Global Optimization in a New Hysteretic Neural Network Model

Weight Identification Through Global Optimization in a New Hysteretic Neural Network Model

... simple artificial neural networks are unable to retain information from their past state to influence their ...hysteresis model is developed in the framework of convolutional neural ...

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A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision

A Spiking Neural Network Model of Depth from Defocus for Event-based Neuromorphic Vision

... spiking neural network model that solves the depth from focus efficiently by exploit- ing an event-based representation amenable to neuromorphic hardware ...The network operates on visual data ...

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Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

Bayesian inference of non-linear multiscale model parameters accelerated by a Deep Neural Network

... solution. Artificial Neural Networks (ANNW) have been used in the literature to reproduce the homogenized behavior predicted by computational homogenization methods, either by approximating the strain ...

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Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

Modeling and Optimization of M-cresol Isopropylation for Obtaining N-thymol: Combining a Hybrid Artificial Neural Network with a Genetic Algorithm

... ANN model is presented below: 1 – The generation index (N gen ) is set to zero and a population of chromosomes is randomly ...based model in order to obtain the corresponding output ...

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Thermodynamics-based Artificial Neural Networks for constitutive modeling

Thermodynamics-based Artificial Neural Networks for constitutive modeling

... of artificial neural networks models to replace constitutive laws and predict the material response at the material point level was ...the model, which we refer to as Thermodynamics-based ...

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Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

... and, where large data sets are available, predicting energies with an accuracy that approaches or exceeds the baseline accuracy of approximate DFT. 29 , 33 , 34 ML models have excelled in design for narrow composition ...

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An Artificial neural network approach for predicting architectural speech security (L)

An Artificial neural network approach for predicting architectural speech security (L)

... STATISTICA Neural Networks 9 ...STATISTICA Neural Networks program by altering the model’s ...the network when an entire network design procedure was ...

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Thermal field prediction in DED manufacturing process using Artificial Neural Network

Thermal field prediction in DED manufacturing process using Artificial Neural Network

... wher here t e temper emperatur ature is abo e is abovve 1720 K (liquidus). e 1720 K (liquidus). This study was exploratory. It is hereafter proposed to highlight the impact of the architecture of the sequential ANN ...

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On line monitoring of crystallization process using FBRM and artificial neural network

On line monitoring of crystallization process using FBRM and artificial neural network

... driving force is not fully understood which makes it difficult to model (experiments are the best guide). Usually, the instantaneous formation of many nuclei can be observed “crashing out” of the solution. You ...

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