scholarly journals Genetic Optimization of Neural Networks for Person Recognition based on the Iris

2012 ◽  
Vol 10 (2) ◽  
Author(s):  
Patricia Melin ◽  
Victor Herrera ◽  
Danniela Romero ◽  
Fevrier Valdez ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Genetic Optimization ◽  
Person Recognition

Pumping System Control Using Genetic Optimization and Neural Networks

1995 ◽  
Vol 28 (10) ◽  
pp. 685-690 ◽  
Author(s):  
Lindell E. Ormsbee ◽  
Srinivasa L. Reddy
Keyword(s):  
Neural Networks ◽  
System Control ◽  
Genetic Optimization ◽  
Pumping System

scholarly journals Genetic Optimization of Neural Networks for Person Recognition Based on the Iris

2012 ◽  
Vol 10 (2) ◽  
pp. 309 ◽  
Author(s):  
Patricia Melin ◽  
Victor Herrera ◽  
Danniela Romero ◽  
Fevrier Valdez ◽  
Oscar Castillo
Keyword(s):  
Neural Networks ◽  
Genetic Optimization ◽  
Person Recognition

Genetically Optimized Multi-Layer Fuzzy Polynomial Neural Networks: Analysis and Design

2006 ◽  
Vol 10 (1) ◽  
pp. 35-49 ◽  
Author(s):  
Sung-Kwun Oh ◽  
◽  
Witold Pedrycz ◽  
Ho-Sung Park ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Design Procedure ◽  
Least Square Method ◽  
Least Square ◽  
Genetic Optimization ◽  
Analysis And Design ◽  
Networks Analysis ◽  
The One ◽  
Neurofuzzy Networks ◽  
Selection Of

In this study, we introduce a new category of neurofuzzy networks – Fuzzy Polynomial Neural Networks and develop a comprehensive design methodology involving mechanisms of genetic optimization, and genetic algorithms, in particular. The augmented genetically optimized FPNN (referred to as gFPNN) is a structurally optimized architecture which comes with a higher level of flexibility in comparison to the one we have encountered in the conventional FPNN. The GA-based design procedure being applied to each layer of FPNN leads to the selection of preferred nodes (or FPNs) available within the FPNN. In the sequel, two general optimization mechanisms are explored. First, the structural optimization is realized via GAs whereas for the ensuing detailed parametric optimization is carried out in the setting of a standard least square method-based learning. The performance of the gFPNN is quantified through experimentation where we use a number of modeling benchmarks – synthetic and experimental data already experimented with in fuzzy or neurofuzzy modeling.

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