[PDF] Top 20 A method based on neural networks for generating solar radiation map
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A method based on neural networks for generating solar radiation map
... global solar radiation (GSR) is important in most solar energy applications, particularly in design methods, in system characterization and in decision making for energy ...methodology based ... See full document
12
CNNcon: improved protein contact maps prediction using cascaded neural networks.
... The method performs better on prediction accuracy than other compared state-of-the-art ...CNNcon method has better consistency and stability on prediction accuracy as protein length ...CNNcon ... See full document
7
A comparison of energy consumption prediction models based on neural networks of a bioclimatic building
... focus on bioclimatic architectures for buildings to reduce the indoor consumption of ...designed based on the local climate ...passive solar technologies where heating and cooling techniques ... See full document
24
Neural models project for solar radiation and atmospheric temperature forecast
... of networks generated on a second MOGA execution where only the images statistically classified as cloudy were removed from the data sets ...be based in the homogeneity and significance of the ... See full document
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A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature
... 158 on the hsvR pixel intensity scale. RCT method. This method is a histogram-iteration form, presented in [52], of an iterative thresholding algorithm [53] that we denote by RCT in the ...the ... See full document
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Desenvolvimento e avaliação de modelos de redes neurais para estimação da irradiação solar diária em Córdoba, Argentina
... develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and ... See full document
6
Res. Biomed. Eng. vol.33 número3
... system based on multi-layer perceptrons (MLPs), capable of accurately identifying several types of indoor and outdoor activities (Semwal et ...system based on artiicial neural ... See full document
8
IMPACT OF ALIFE SIMULATION OF DARWINIAN AND LAMARCKIAN EVOLUTIONARY THEORIES
... focused on systems, which can mimic nature and its laws and therefore it is more related to biology, while the latter is mainly focused on how human intelligence can be replicated, and therefore, it is more ... See full document
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A New Approach for Time Series Forecasting: Bayesian Enhanced by Fractional Brownian Motion with Application to Rainfall Series
... are based on stochastic techniques that assume non- linear relationship among data that reproduce the benchmark chaotic time series and rainfall data only in statistical ... See full document
8
Neural Network Controller for Two links- Robotic Manipulator Control with Different Load
... control, Neural Network, Neuro Fuzzy and because that they can control nonlinear systems that would be difficult or impossible to model ...a neural network approach for the motion control of constrained ... See full document
6
OPTIMIZATION OF OPERATING PARAMETERS FOR EDM PROCESS BASED ON THE TAGUCHI METHOD AND ARTIFICIAL NEURAL NETWORK
... Taguchi method uses a special design of orthogonal array to study the entire parameter space with only a small number of ...predicted based on the above ... See full document
9
A cluster method for finding node sets / sub-networks based on between- node similarity in sets of adjacency nodes: with application in finding sub-networks in tumor pathways
... at certain levels (bold results) are acceptable. There are mainly four sub-networks, i.e., node sets (1,2,3,4,5,6), (35,36,37,38,39,40), (10,11,12,13,14,15,16,17,18), (24,25,26,27,28,29,30,31). Therefore the ... See full document
11
Multivariate synthetic streamflow generation using a hybrid model based on artificial neural networks
... The combined stochastic-NN approach outperforms the purely stochastic AR(2) model, particularly for drought related statistics. It can be concluded that the proposed hybrid model is a viable alternative for future ... See full document
14
Diagnosing the success of the construction projects during the initial phases
... stakeholder. On this basis, several models have been introduced which implement different methods for anticipation of the entire goals or a series of goals of ...propagation neural network including 35 ... See full document
12
An Efficient Method for Assessing Water Quality Based on Bayesian Belief Networks
... Therefore, a Bayesian reasoning approach is employed here. This approach incorporates prior knowledge about the possible state of a system, and adds new data in a pre-posterior analysis to produce posterior knowledge of ... See full document
12
SELF TUNING CONTROLLERS FOR DAMPING LOW FREQUENCY OSCILLATIONS
... UPFC is one of the famous FACTs devices that is used to improve power system stability. Figure 1 shows a single machine infinite bus (SMIB) system (Heffron-Philips model of a power system installed with UPFC) with UPFC. ... See full document
8
Sensitization of TiO2 Photoelectrodes Using Copper Phthalocyanine for Hydrogen Production
... process due to their stability in aqueous electrolytes, however, the absorption threshold of this semiconductor does not allow the use of visible light, its band gap energy (3.2eV) allows it to absorb electromagnetic ... See full document
5
Channel Allocation Method for Multi-radio Wireless Mesh Networks based on a Genetic Algorithm
... An important issue in wireless mesh networks is how to allocate channels (i.e., channel allocation) effectively. This concerns the task of allocating channels to all wireless links in the network during the set up ... See full document
6
A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull
... our method, it is possible that a node does not obtain an estimated position when it does not contain anchors in its ...depends on the anchors ...focus on the time during which a node is located with ... See full document
9
Numerical Solution of PDE’s Using Deep Learning
... Nonetheless, seen as function approximators, NN are extremely powerful objects. This fact has rigorous proof within the Theorem of Universal Approximation, from which the proof by [Cybenko, 1989] is the most famous. In ... See full document
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