• Corpus ID: 61288267

Neural Network Toolbox™ User's Guide

@inproceedings{Beale2015NeuralNT,
  title={Neural Network Toolbox{\texttrademark} User's Guide},
  author={Mark H. Beale and Martin T. Hagan and Howard B. Demuth},
  year={2015}
}
ANNFAA: artificial neural network-based tool for the analysis of Federal Aviation Administration’s rigid pavement systems
ABSTRACT Three-dimensional Finite Element (3D-FE) stress computations involved in the current rigid airport pavement design methodology, are time consuming when considering top-down cracking failure
Control Power Optimization using Artificial Intelligence for Forward Swept Wing and Hybrid Wing Body Aircraft
Many futuristic aircraft such as the Hybrid Wing Body have numerous control surfaces that can result in large hinge moments, high actuation power demands, and large actuator forces/moments. Also,
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The proposed MLP proposed model appears to be a suitable tool for prediction of gate roadways stability in longwall mining by predicting values close enough to the measured ones with an acceptable range of correlation.
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This review concludes with a discussion of the literature review’s methodology, findings, and recommendations for further study.
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BackgroundIn the last decades, everal runoff-erosion models have been proposed to estimate soil erosion, which may lead to loss of fertile land and increase sedimentation and pollution in water
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Dissertacao (mestrado)—Universidade de Brasilia, Faculdade de Tecnologia, Departamento de Engenharia Eletrica, 2012.
Substitution for lost one-hour means of the geomagnetic elements for the first half of the 20-th century at the Hurbanovo Geomagnetic Observatory by means of neural networks
Abstract The existence of long-acting observatories by itself does not guarantee that their historical magnetograms are available or complete. In the archive of the Hurbanovo Geomagnetic Observatory
Specifications for Modelling of the Phenomenon of Compression of Closed-Cell Aluminium Foams with Neural Networks
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The research shows that the phenomenon of the compression of aluminium foam is able to be described by neural networks within the frames of made assumptions and allowed for the determination of detailed specifications of structure and learning parameters for building models with good-quality accuracy and robustness.
Application of Probabilistic Neural Networks Using High-Frequency Components’ Differential Current for Transformer Protection Schemes to Discriminate between External Faults and Internal Winding Faults in Power Transformers
Internal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network
Efficiency of artificial neural networks for glacier ice-thickness estimation: a case study in western Himalaya, India
Abstract Knowledge of glacier volume is crucial for ice flow modelling and predicting the impacts of climate change on glaciers. Rugged terrain, harsh weather conditions and logistic costs limit
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