Multi-criteria decision making based architecture selection for single-hidden layer feedforward neural networks

2017 ◽  
Vol 10 (4) ◽  
pp. 655-666 ◽  
Author(s):  
Ran Wang ◽  
Haoran Xie ◽  
Jiqiang Feng ◽  
Fu Lee Wang ◽  
Chen Xu
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Feedforward Neural Networks ◽  
Multi Criteria Decision Making ◽  
Selection For ◽  
Hidden Layer

Site selection for artificial reefs using a new combine Multi-Criteria Decision-Making (MCDM) tools for coral reefs in the Kish Island – Persian Gulf

2015 ◽  
Vol 111 ◽  
pp. 92-102 ◽  
Author(s):  
Seyed Hassan Mousavi ◽  
Afshin Danehkar ◽  
Mohammad Reza Shokri ◽  
Hadi Poorbagher ◽  
Danial Azhdari
Keyword(s):  
Decision Making ◽  
Coral Reefs ◽  
Site Selection ◽  
Persian Gulf ◽  
Artificial Reefs ◽  
Multi Criteria Decision Making ◽  
Selection For

scholarly journals Group $L_{1/2}$ Regularization for Pruning Hidden Layer Nodes of Feedforward Neural Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 9540-9557 ◽  
Author(s):  
Habtamu Zegeye Alemu ◽  
Junhong Zhao ◽  
Feng Li ◽  
Wei Wu
Keyword(s):  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Hidden Layer

On Functional Approximation with Normalized Gaussian Units

1994 ◽  
Vol 6 (2) ◽  
pp. 319-333 ◽  
Author(s):  
Michel Benaim
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Asymptotic Properties ◽  
Learning Rule ◽  
Adaptive Algorithms ◽  
Feedforward Neural Networks ◽  
Hybrid Learning ◽  
Universal Approximation ◽  
Ode Method ◽  
Hidden Layer

Feedforward neural networks with a single hidden layer using normalized gaussian units are studied. It is proved that such neural networks are capable of universal approximation in a satisfactory sense. Then, a hybrid learning rule as per Moody and Darken that combines unsupervised learning of hidden units and supervised learning of output units is considered. By using the method of ordinary differential equations for adaptive algorithms (ODE method) it is shown that the asymptotic properties of the learning rule may be studied in terms of an autonomous cascade of dynamical systems. Some recent results from Hirsch about cascades are used to show the asymptotic stability of the learning rule.

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