[PDF] Top 20 Artificial Neural Network Models for Alternative Investments
Has 10000 "Artificial Neural Network Models for Alternative Investments" found on our website. Below are the top 20 most common "Artificial Neural Network Models for Alternative Investments".
Artificial Neural Network Models for Alternative Investments
... node. For the Kolmogorov- Smirnov test, only the size of the largest difference ...optimal for one of the various Quartic Utility functions - making their higher moments more attractive by a simple linear ... Voir le document complet
372
Artificial neural network surrogate development of equivalence models for nuclear data uncertainty propagation in scenario studies
... equivalence models, built for calculating fresh fuel enrichment (for instance plutonium content in PWR MOX) so as to be representative of nominal fuel behavior, are very ...equivalence models ... Voir le document complet
16
Flux prediction using artificial neural network (ANN) for the upper part of glycolysis
... mathematical models exist to study biological systems [ 17 , 18 ...assay for new enzymes or to find a new one. In some cases, for example, following enzyme reaction through spectrophotometers or ... Voir le document complet
16
Design of a thermoacoustic sensor for low intensity ultrasound measurements based on an artificial neural network
... balance for low intensity ultrasound measurement can easily surpass ...situations. For example, an ultrasound holder with an array of transducers is commonly used in biological experiments to stimulate ... Voir le document complet
22
A PCA spatial pattern based artificial neural network downscaling model for urban flood hazard assessment
... potential alternative to this reference approach would be to consider a single ANN that would be able to downscale all the cells of the mesh, one at a time, by including in its input specific information from each ... Voir le document complet
33
Visco-elastic portrayal of bituminous materials: artificial neural network approach
... the artificial neural network as an alternative to performing the test to cover the wide spectrum of factors that are known to influence the dynamic ...Further, artificial neural ... Voir le document complet
10
Artificial neural network modeling of water table depth fluctuations.
... that for 1 month ahead forecast, all the proposed models can provide satisfactory predictions (LDC ⬍ ...0.2) for both large and moderate water table fluctu- ...effective for deep water table ... Voir le document complet
12
Comparing feedforward and recurrent neural network architectures with human behavior in artificial grammar learning
... compared models and humans performance as a function of sequence length ...B,D). For each grammar, we tested a Bayesian ANOVA having as factors sequence LENGTH, and AGENT (human, FF or ...LENGTH for ... Voir le document complet
16
Neural network applications to reservoirs: Physics-based models and data models
... explored for a better generalization and regularization of ANN: (a) arrest in the training of the system before convergence, (b) moderating the ...the network, but if data are lacking , cross validation is ... Voir le document complet
15
Use of artificial neural networks for helicopter load monitoring
... the neural network will be investigated. Different models other than the multi- layer perceptron but still within the realm of machine learning may prove to be better analytical tools for this ... Voir le document complet
13
Modeling the creep compliance of asphalt concrete using the artificial neural network technique
... the artificial neural network as an alternative to performing the ...Further, artificial neural network models were found to yield satisfactory accuracy; an ... Voir le document complet
9
Bootstrapped artificial neural networks for the seismic analysis of structural systems
... curves. Artificial Neural Network (ANN) empirical regression models are employed as fast-running surrogates of the (long-running) Finite Element Models (FEMs) that are typically adopted ... Voir le document complet
37
An Artificial neural network approach for predicting architectural speech security (L)
... ANN MODELS The inputs of the ANN models for all the four prediction situations are the octave band (250 Hz – 8 kHz) S/N ratios, that is to say, the difference in the transmitted speech level and the ... Voir le document complet
19
Neural network stochastic simulation applied for quantifying uncertainties
... artificial neural networks which mimic the neurons behavior of human ...a neural network algorithm to faster the search of the ...geophysics for locating ground targets from ... Voir le document complet
10
Stochastic downscaling of precipitation with neural network conditional mixture models
... QQ-plots on a logarithmic scale of the simulated precipitation versus observations >1 mm on the Orange test set for left the hybrid Pareto conditional mixture and right the benchmark mod[r] ... Voir le document complet
16
An artificial neural network analysis of factors controlling ecosystem metabolism in the coastal ocean
... even for data collected within the same program, simple correlations often do not suffice to explain the determining factors due to the wide variability in the parameters ...0.98 for the network when ... Voir le document complet
12
Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network
... © 2013 Elsevier B.V. All rights reserved. 1. Introduction Considering the present energy balance [1] , buildings design has to integrate thermal performance. This notion takes into account energy savings and comfort of ... Voir le document complet
9
Surface electrocardiogram reconstruction from intracardiac electrograms using a dynamic time delay artificial neural network.
... ECG, for three different approaches: D_TDNN, I_TDNN and ...D_RLS, for which the measured dispersion is always ...obtained for Type ...ECG. For instance, we observe that the RR interval is well ... Voir le document complet
10
Alternative Models for Stock Price Dynamics
... Comparing many different specifications of pure diffusion multi-factor models and jump diffusion models, we find that 1 log linear models have to be extented to 2 factors with feedback i[r] ... Voir le document complet
43
Time Dependent Neural Network Models for Detecting Changes of State in Earth and Planetary Processes
... AR models represented by a special kind of neuro-fuzzy neural ...of models composed by sets of time lag terms from the time series, as well as prediction functions represented by neuro-fuzzy ...the ... Voir le document complet
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