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In this paper, we present a simulation-based methodology using neural networks to characterize ultrasonic transducers.
During the past eight years or so, a methodology using neural networks and fuzzy logic has been developed to assist geologists in the characterization of these complex reservoirs.
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This paper suggests a multi-parameter fragility methodology using artificial neural network to generate bridge-specific fragility curves without grouping the bridge classes.
The size of the structure is optimized using the response surface methodology, using artificial neural networks metamodels and simulated annealing optimization techniques.
An interpolation methodology, using artificial neural networks (ANN), was developed to include variable plan dimensions and roof slopes in the set of parameters considered in earlier interpolation studies.
In this paper, a methodology using modular neural networks (MNN) is proposed to simulate the nitrate concentrations in an agriculture-dominated aquifer.
This work describes the methodology of input output-linearization usinput output-linearizationand gives an extended simusingve case study of its applicationeuralrajectory tracking of a batch processization reactor.
This paper porposes a new methodology to model ill-defined processing operations using neural nets (NNs).
Two different classification models are employed using neural networks to evaluate the methodology.
This paper presents the modeling methodology of Zn diffusion process utilized for high-speed avalanche photodiode fabrication using neural networks.
But before presenting the methodology proposed for the irradiance forecast model, we outline the research done so far for forecasting irradiance using neural network approaches.
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