Name
Title | ||
|---|---|---|
High-order neural network structure selection for function approximation applications using genetic algorithms |
Abstract | ||
|---|---|---|
Neural network literature for function approximation is by now sufficiently rich. In its complete form, the problem entails both parametric (i.e., weights determination) and structural learning (i.e., structure selection). The majority of works deal with parametric uncertainty assuming knowledge of the appropriate neural structure. In this paper we present an algorithmic approach to determine the structure of high order neural networks (HONNs), to solve function approximation problems. The method is based on a genetic algorithm (GA) and is equipped with a stable update law to guarantee parametric learning. Simulation results on an illustrative example highlight the performance and give some insight of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/TSMCB.2003.811767 | IEEE Transactions on Systems, Man, and Cybernetics, Part B |
Keywords | Field | DocType |
learning artificial intelligence,genetic algorithm,neural nets,function approximation,genetic algorithms,neural network | Intelligent control,Feedforward neural network,Mathematical optimization,Function approximation,Computer science,Parametric statistics,Types of artificial neural networks,Artificial intelligence,Deep learning,Artificial neural network,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
34 | 1 | 1083-4419 |
Citations | PageRank | References |
16 | 1.07 | 13 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George A. Rovithakis | 1 | 749 | 45.73 |
| I. Chalkiadakis | 2 | 16 | 1.41 |
| M. E. Zervakis | 3 | 41 | 4.92 |