A comparative study on the estimation of evapotranspiration using backpropagation neural network: Penman–Monteith method versus pan evaporation method

2011 ◽  
Vol 9 (4) ◽  
pp. 413-424 ◽  
Author(s):  
Sheng-Feng Kuo ◽  
Feng-Wen Chen ◽  
Pei-Yu Liao ◽  
Chen-Wuing Liu
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Pan Evaporation ◽  
Backpropagation Neural Network ◽  
Evaporation Method

scholarly journals Comparative Study of Artificial Neural Network Based Channel Equalization Methods for MmWave Communications

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Diego Fernando Carrera ◽  
Cesar Vargas-Rosales ◽  
Noe M. Yungaicela-Naula ◽  
Leyre Azpilicueta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Comparative Study ◽  
Channel Equalization ◽  
Artificial Neural

scholarly journals Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2998
Author(s):  
Xinyong Zhang ◽  
Liwei Sun
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Prediction Model ◽  
Bayesian Regularization ◽  
Backpropagation Neural Network ◽  
Swarm Optimization ◽  
Modal Frequency ◽  
Supporting Structure ◽  
First Order ◽  
Machine Structure

Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

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