Reconfigurable Hardware to Radionuclide Identification Using Subtractive Clustering

2011 ◽  
pp. 387-398
Author(s):  
Marcos Santana Farias ◽  
Nadia Nedjah ◽  
Luiza de Macedo Mourelle
Keyword(s):  
Reconfigurable Hardware ◽  
Subtractive Clustering

Fault Detection Methods for Interconnects in Reconfigurable Hardware

2014 ◽  
Vol 2 (4) ◽  
pp. 12-19
Author(s):  
Pradeep C ◽  
◽  
Radhakrishnan R ◽  
Keyword(s):  
Fault Detection ◽  
Reconfigurable Hardware ◽  
Detection Methods

Parallel real-time garbage collection of multiple heaps in reconfigurable hardware

2015 ◽  
Vol 49 (11) ◽  
pp. 117-127 ◽  
Author(s):  
David F. Bacon ◽  
Perry Cheng ◽  
Sunil Shukla
Keyword(s):  
Real Time ◽  
Garbage Collection ◽  
Reconfigurable Hardware

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

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