Fatigue life modeling using Neural Networks
Modeling flexibility using artificial neural networks
... artificial neural networks (ANNs) pre- sented in (Förderer et ...DERs using generated or measured load profiles and state data relating to the corresponding DERs as training ...
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UAV Trajectory Modeling Using Neural Networks
... a neural network approach for modeling sUAV’s dynamics and control system for trajectory ...A neural network composed of nine inputs, 20 hidden neurons, and six outputs was proposed and trained ...
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Time series modeling and synchronization using neural networks
... the modeling, control and forecast of real-world systems from a set of measured ...then, using the obtained model for predicting the future, or for controlling the system’s ...
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Modeling Information Technology Competency using Neural Networks
... Fig 2:German Credit Selected Variables accuracy for NN and Regression 3. NEUROCITE SYSTEM NeuroCite is a Web-based information system whose acronym represents NN for Computer and Information Technology Competency. The ...
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Structured prediction and generative modeling using neural networks
... by using the right layers in a neural network, we can create learned filters which start randomly and are trained by gradient ...audio modeling (Dieleman and Schrauwen, 2014), or text classification ...
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Nonlinear Evapotranspiration Modeling Using Artificial Neural Networks
... of using complex ANN models, it is necessary to show the results obtained using MLR ...optimum neural network was selected based on criteria such that the model has minimum RMSE and maximum R 2 ...
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Modeling Helicopter Dynamic Loads. Using Artificial Neural Networks
... To find a combination of inputs that appears to yield the best performance, the 28 inputs are categorized in accordance with their expected importance (level of [r] ...
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Modeling Newswire Events using Neural Networks for Anomaly Detection
... of modeling event composition can help processing event data in general and can be applied to other tasks like finding co-referent ...for modeling semantic ...
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Modeling brand choice using boosted and stacked neural networks
... model using only the data from this sample; a second model is built using only the data from a second random sample drawn from the original dataset, and so ...
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Engineering Project Management Modeling Using Artificial Neural Networks
... artificial neural networks (ANN) and based on their self- study, self-adjustment and nonlinear mapping (activation) function of the ANN inputs to outputs the performance evaluation model of engineering ...
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Nonlinear Modeling Using Neural Networks for Trading the Soybean Complex
... models, using artificial intelligence and nonlinear ...different neural network architectures and inputs to discover desirable returns for both speculative trading and hedging are ...
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Generative Dependency Language Modeling Using Recurrent Neural Networks
... One of the first attempts of creating language models that use syntactic information date back to 1997 when Chelba [4] developed a language model that would, similarly to our proposed approach, process a sentence left to ...
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Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks - ASCE
... the fatigue life of asphalt mixtures is a difficult task due to the complex nature of materials behavior under various loading and environmental ...artificial neural network (ANN) in predicting the ...
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Computational modeling with spiking neural networks
... Fig. 37.17 Schematic illustration of the evolving spiking neural network architecture (eSNN). Real-valued vector el- ements are mapped into the time domain using rank order population encoding based on ...
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Modeling trajectories with recurrent neural networks
... However, it uses MDP which is actually a first-order Markov chain and hence BIRL fails to capture the long term dependency in the past. MEIRL performs not very well, as it models the likelihood of the trajectory based on ...
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Modeling Interestingness with Deep Neural Networks
... learned using Maximum Likelihood Estimation in an unsupervised manner, ...space using a supervised learning method, ...trained using an objective that is tailored to the interestingness ...
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Improving Language Modeling using Densely Connected Recurrent Neural Networks
... [email protected] Abstract In this paper, we introduce the novel con- cept of densely connected layers into re- current neural networks. We evaluate our proposed architecture on the Penn Treebank ...
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Modeling Consideration Sets and Brand Choice Using Artificial Neural Networks
... In sum, artificial neural networks are applied in marketing research, though with mixed results. To our knowledge, there have been some attempts so far to link ANNs with actual choice behavior, but none of ...
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Time Series Modeling of River Flow Using Wavelet Neural Networks
... An ANN, can be defined as a system or mathematical model consisting of many nonlinear artificial neurons running in parallel, which can be generated, as one or multiple layered. Although the concept of artificial neu- ...
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Modeling of Electric Power Transformer Using Complex- Valued Neural Networks
... simulation using one model for each type of equipment ...equipment modeling – modeling based on Complex-Valued Neural Networks (CVNN) – gives an opportunity to build a high-quality ...
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