Name
Abstract | ||
|---|---|---|
This paper proposes a novel hybrid algorithm to determine the parameters (number of neurons, centers, widths and weights) of radial basis function neural networks automatically. In this work, a hybrid algorithm combines the multi-encoding genetic algorithm (MGA) and the back propagation (BP) algorithm to form a hybrid learning algorithm (MGA-BP) for training radial basis function networks (RBFNs), which adapts to the network structure and updates its weights by choosing a special fitness function. The proposed method is used to deal with non-linear identification problems, and the results obtained are compared with existent bibliography, showing an improvement over the published methods. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICSMC.2009.5346802 | SMC |
Keywords | DocType | ISSN |
radial basis function neural networks,self-generate,hybrid mga-bp algorithm,multiencoding genetic algorithm,radial basis function networks,identification,backpropagation algorithm,hybrid learning algorithm,nonlinear identification problems,backpropagation,multi-encoding,genetic algorithm,genetic algorithms,rbfn,back propagation,data mining,hybrid algorithm,radial basis function network,gallium,artificial neural networks,testing,fitness function,optimization | Conference | 1062-922X E-ISBN : 978-1-4244-2794-9 |
ISBN | Citations | PageRank |
978-1-4244-2794-9 | 0 | 0.34 |
References | Authors | |
12 | 2 | |