Growing compact RBF networks using a genetic algorithm

2003 ◽  
Author(s):  
Ada.M.S. Barreto ◽  
H.J.C. Barbosa ◽  
N.F.F. Ebecken
Keyword(s):  
Genetic Algorithm ◽  
Rbf Networks

VARIABLE SELECTION IN NONLINEAR MODELING BASED ON RBF NETWORKS AND EVOLUTIONARY COMPUTATION

2010 ◽  
Vol 20 (05) ◽  
pp. 365-379 ◽  
Author(s):  
PANAGIOTIS PATRINOS ◽  
ALEX ALEXANDRIDIS ◽  
KONSTANTINOS NINOS ◽  
HARALAMBOS SARIMVEIS
Keyword(s):  
Genetic Algorithm ◽  
Variable Selection ◽  
Fitness Function ◽  
Nonlinear Modeling ◽  
Time Series Prediction ◽  
Free Variable ◽  
Tuning Parameter ◽  
Rbf Neural Networks ◽  
Rbf Networks ◽  
Final Prediction Error

In this paper a novel variable selection method based on Radial Basis Function (RBF) neural networks and genetic algorithms is presented. The fuzzy means algorithm is utilized as the training method for the RBF networks, due to its inherent speed, the deterministic approach of selecting the hidden node centers and the fact that it involves only a single tuning parameter. The trade-off between the accuracy and parsimony of the produced model is handled by using Final Prediction Error criterion, based on the RBF training and validation errors, as a fitness function of the proposed genetic algorithm. The tuning parameter required by the fuzzy means algorithm is treated as a free variable by the genetic algorithm. The proposed method was tested in benchmark data sets stemming from the scientific communities of time-series prediction and medicinal chemistry and produced promising results.

Train RBF networks with a hybrid genetic algorithm

2021 ◽  
Author(s):  
Ioannis G. Tsoulos ◽  
Nikolaos Anastasopoulos ◽  
Georgios Ntritsos ◽  
Alexandros Tzallas
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Rbf Networks

Adaptive Genetic Algorithm for Multilayer RBF Network and its Application on Real Function Approximation

2013 ◽  
Vol 380-384 ◽  
pp. 1166-1169 ◽  
Author(s):  
Yan Hui Wang ◽  
Kun Zhang
Keyword(s):  
Genetic Algorithm ◽  
Computer Simulation ◽  
Real Function ◽  
Function Approximation ◽  
Clustering Algorithm ◽  
Least Squares Method ◽  
Adaptive Genetic Algorithm ◽  
Rbf Networks ◽  
Rbf Network ◽  
Hidden Layer

With the application of adaptive genetic algorithm to the training of multi-layer RBF networks and the optimization of the hidden layer centers and width values and using regularized least squares method, weight vectors is obtained. Computer simulation shows that the precision of real function approximation by this algorithm is much higher than the precision by clustering algorithm for multi-layer RBF networks.

Training RBF Networks using DDA Algorithm Combined with Genetic Algorithm

2008 ◽  
Author(s):  
G. Khensous ◽  
B. Messabih ◽  
N. Benamrane ◽  
Hichem Arioui ◽  
Rochdi Merzouki ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Rbf Networks
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