scholarly journals Optimal design of artificial neural networks by a multi-objective strategy: groundwater level predictions

2006 ◽  
Vol 51 (3) ◽  
pp. 502-523 ◽  
Author(s):  
ORAZIO GIUSTOLISI ◽  
VINCENZO SIMEONE
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimal Design ◽  
Groundwater Level ◽  
Multi Objective ◽  
Artificial Neural

Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks

2013 ◽  
Vol 58 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Manuel Cruz-Ramírez ◽  
César Hervás-Martínez ◽  
Juan Carlos Fernández ◽  
Javier Briceño ◽  
Manuel de la Mata
Keyword(s):  
Neural Networks ◽  
Liver Transplantation ◽  
Artificial Neural Networks ◽  
Patient Survival ◽  
Multi Objective ◽  
Artificial Neural

An Optimal Design Method for Artificial Neural Networks by Using the Design of Experiments

2007 ◽  
Vol 11 (6) ◽  
pp. 593-599 ◽  
Author(s):  
Eiichi Inohira ◽  
◽  
Hirokazu Yokoi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimal Design ◽  
Design Method ◽  
Feedforward Neural Networks ◽  
Learning Rate ◽  
Design Parameters ◽  
Approximation Accuracy ◽  
Artificial Neural ◽  
Trained Neural Network

This paper presents a method to optimally design artificial neural networks with many design parameters using the Design of Experiment (DOE), whose features are efficient experiments using an orthogonal array and quantitative analysis by analysis of variance. Neural networks can approximate arbitrary nonlinear functions. The accuracy of a trained neural network at a certain number of learning cycles depends on both weights and biases and its structure and learning rate. Design methods such as trial-and-error, brute-force approaches, network construction, and pruning, cannot deal with many design parameters such as the number of elements in a layer and a learning rate. Our design method realizes efficient optimization using DOE, and obtains confidence of optimal design through statistical analysis even though trained neural networks very due to randomness in initial weights. We apply our design method three-layer and five-layer feedforward neural networks in a preliminary study and show that approximation accuracy of multilayer neural networks is increased by picking up many more parameters.

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