A GODFIP Control Algorithm for an IRC Grain Dryer

Dai, Aini; Zhou, Xiaoguang; Liu, Xiangdong

doi:10.1155/2017/1406292

Cited by 5 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The NARX model is obtained by removing the moving-average term when the error sequence is not considered. The NARX model is universally used in the uncertainty propagation of systems to reveal their dynamic behavior from an observed time series; its performance has been proven superior and it has been successfully employed to solve various types of complex dynamic system problems (Chan et al, 2015; Dai et al, 2017; Ng et al, 2014).…”

Section: Model Formulationmentioning

confidence: 99%

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

Han

Xiang

et al. 2019

Advances in Structural Engineering

View full text Add to dashboard Cite

To improve the efficiency of reliability calculations for vehicle-bridge systems, we present a surrogate modeling method based on a nonlinear autoregressive with exogenous input artificial neural network model and an important sample, which can forecast responses of dynamic systems, such as vehicle-bridge systems, subjected to stochastic excitations. We also propose a process to analyze the method. A quarter-vehicle model is used to verify the proposed method’s precision, and the nonlinear autoregressive with exogenous input artificial neural network model is used to predict responses of vertical vehicle-bridge systems. The results show that, compared to other training samples, the nonlinear autoregressive with exogenous input artificial neural network model has better prediction accuracy when the sample with the maximum response is considered as an important sample and is used to train the nonlinear autoregressive with exogenous input artificial neural network model, and it requires only two-time numerical simulation (or Monte Carlo simulation) at most, which is used in the training of the nonlinear autoregressive with exogenous input artificial neural network model.

show abstract

Section: Model Formulationmentioning

confidence: 99%

Predictions of vertical train-bridge response using artificial neural network-based surrogate model

Han

Xiang

et al. 2019

Advances in Structural Engineering

View full text Add to dashboard Cite

show abstract

“…The identification methods have been widely used in food preservation (Baloch et al, 2006). Recently, artificial intelligent technique, such as neural networks, has been extensively developed in agricultural field (Morimoto et al, 2009) and postharvest grain process (Dai et al, 2017). This technique has proven to be powerful tools for dealing with complex problems to which conventional mathematical approaches cannot be applied.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of the Drying Process of Corn Grains using Neural Networks

et al. 2019

View full text Add to dashboard Cite

This study examines the model development of the drying process of corn grains as a dynamic system. The appropriate use of a dynamic model for the drying process of corn grains could lead to an effective method for optimizing the system. The optimal control strategy can be determined by predicting the future behaviors of the process using a dynamic model. In this work, the dynamic characteristic of the water loss of corn grains during dynamics treatment of temperature in the drying process was measured in a continuous manner using a precise load cell. The nonlinear autoregressive with external input (NARX) neural network is used to identify and develop a model of dynamic characteristics of the drying process of corn grains. Then for model training and validation, the dynamic responses of the rate of water loss of corn grains to drying temperature were used. A three-layered NARX neural network model consists of 1-10-1 number neurons of each layer with two times delay was successfully developed to identify and make a model such a complex system. The developed model showed the accuracy of the rate water loss of corn grains during the drying process with the mean square error (MSE), and coefficient of determination (R-squared) values are 1.88892 x 10-4 and 0.891594 consecutively.

show abstract