Comparison of a Hybrid Neural Network and Semi-distributed Simulator for Stream Flow Prediction

ISFRAM 2015 ◽  
2016 ◽  
pp. 115-127
Author(s):  
Milad Jajarmizadeh ◽  
Lariyah Mohd Sidek ◽  
Sobri Harun ◽  
Shamsuddin Shahid ◽  
Hidayah Basri
Keyword(s):  
Neural Network ◽  
Stream Flow ◽  
Hybrid Neural Network ◽  
Flow Prediction

scholarly journals Comparison of Back Propagation Neural Network and Genetic Algorithm Neural Network for Stream Flow Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
C. Chandre Gowda ◽  
S. G. Mayya
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Stream Flow ◽  
Prediction Models ◽  
Statistical Tests ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Prediction Ability ◽  
Flow Prediction ◽  
Daily Data

Comparison of stream flow prediction models has been presented. Stream flow prediction model was developed using typical back propagation neural network (BPNN) and genetic algorithm coupled with neural network (GANN). The study uses daily data from Nethravathi River basin (Karnataka, India). The study demonstrates the prediction ability of GANN. The statistical tests show that GANN model performs much better when compared to BPNN model.

A hybrid neural network goal attain optimization for failed sensor(s) radiation pattern in linear array

2019 ◽  
Vol 4 (4) ◽  
pp. 101-109
Author(s):  
Navaamsini Boopalan ◽  
Agileswari K. Ramasamy ◽  
Farrukh Hafiz Nagi
Keyword(s):  
Neural Network ◽  
Radiation Pattern ◽  
Array Signal Processing ◽  
Linear Array ◽  
Signal To Noise Ratio ◽  
Beam Pattern ◽  
Sensor Failure ◽  
Signal To Noise ◽  
Hybrid Neural Network ◽  
Noise Ratio

Array sensors are widely used in various fields such as radar, wireless communications, autonomous vehicle applications, medical imaging, and astronomical observations fault diagnosis. Array signal processing is accomplished with a beam pattern which is produced by the signal's amplitude and phase at each element of array. The beam pattern can get rigorously distorted in case of failure of array element and effect its Signal to Noise Ratio (SNR) badly. This paper proposes on a Hybrid Neural Network layer weight Goal Attain Optimization (HNNGAO) method to generate a recovery beam pattern which closely resembles the original beam pattern with remaining elements in the array. The proposed HNNGAO method is compared with classic synthesize beam pattern goal attain method and failed beam pattern generated in MATLAB environment. The results obtained proves that the proposed HNNGAO method gives better SNR ratio with remaining working element in linear array compared to classic goal attain method alone. Keywords: Backpropagation; Feed-forward neural network; Goal attain; Neural networks; Radiation pattern; Sensor arrays; Sensor failure; Signal-to-Noise Ratio (SNR)

Multi-attention 3D Residual Neural Network for Origin-Destination Crowd Flow Prediction

2020 ◽  
Author(s):  
Jiaman Ma ◽  
Jeffrey Chan ◽  
Sutharshan Rajasegarar ◽  
Goce Ristanoski ◽  
Christopher Leckie
Keyword(s):  
Neural Network ◽  
Flow Prediction
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