Study of the Robustness of a Meta-Algorithm for the Estimation of Parameters in Artificial Neural Networks Design

2008 ◽  
Author(s):  
Elisabet Parras-Gutierrez ◽  
M. Jose del Jesus ◽  
Victor M. Rivas ◽  
Juan J. Merelo
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Estimation Of Parameters ◽  
Artificial Neural

A Proposal for Parameter-Free Surrogate Building Algorithm Using Artificial Neural Networks

2022 ◽  
pp. 749-782
Author(s):  
Srinivas Soumitri Miriyala ◽  
Kishalay Mitra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Industrial Process ◽  
Sampling Technique ◽  
Sample Size Determination ◽  
Estimation Of Parameters ◽  
Training Sample Size ◽  
Online Implementation ◽  
Artificial Neural

Surrogate models, capable of emulating the robust first principle based models, facilitate the online implementation of computationally expensive industrial process optimization. However, the heuristic estimation of parameters governing the surrogate building often renders them erroneous or under-trained. Current work aims at presenting a novel parameter free surrogate building approach, specifically focusing on Artificial Neural Networks. The proposed algorithm implements Sobol sampling plan and intelligently designs the configuration of network with simultaneous estimation of optimal transfer function and training sample size to prevent overfitting and enabling maximum prediction accuracy. A novel Sample Size Determination algorithm based on a potential concept of hypercube sampling technique adds to the speed of surrogate building algorithm, thereby assuring faster convergence. Surrogates models for a highly nonlinear industrial sintering process constructed using the novel algorithm resulted in 7 times faster optimization.

A Proposal for Parameter-Free Surrogate Building Algorithm Using Artificial Neural Networks

2018 ◽  
pp. 232-264
Author(s):  
Srinivas Soumitri Miriyala ◽  
Kishalay Mitra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Industrial Process ◽  
Sampling Technique ◽  
Sample Size Determination ◽  
Estimation Of Parameters ◽  
Training Sample Size ◽  
Online Implementation ◽  
Artificial Neural

Surrogate models, capable of emulating the robust first principle based models, facilitate the online implementation of computationally expensive industrial process optimization. However, the heuristic estimation of parameters governing the surrogate building often renders them erroneous or under-trained. Current work aims at presenting a novel parameter free surrogate building approach, specifically focusing on Artificial Neural Networks. The proposed algorithm implements Sobol sampling plan and intelligently designs the configuration of network with simultaneous estimation of optimal transfer function and training sample size to prevent overfitting and enabling maximum prediction accuracy. A novel Sample Size Determination algorithm based on a potential concept of hypercube sampling technique adds to the speed of surrogate building algorithm, thereby assuring faster convergence. Surrogates models for a highly nonlinear industrial sintering process constructed using the novel algorithm resulted in 7 times faster optimization.

FUTURE OF ARTIFICIAL NEURAL NETWORKS IN SPACE EXPLORATION AND SPACE ENGINEERING

2018 ◽  
pp. 6-11
Author(s):  
Kobiljon Kh. Zoidov ◽  
◽  
Svetlana V. Ponomareva ◽  
Daniel I. Serebryansky ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Exploration ◽  
Artificial Neural
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