Name
Abstract | ||
|---|---|---|
The use of Recurrent Neural Networks is not as extensive as Feedforward Neural Networks. Training algorithms for Recurrent Neural Networks, based on the error gradient, are very unstable in their search for a minimum and require much computational time when the number of neurons is high. The problems surrounding the application of these methods have driven us to develop new training tools. In this paper, we present a Real-Coded Genetic Algorithm that uses the appropriate operators for this encoding type to train Recurrent Neural Networks. We describe the algorithm and we also experimentally compare our Genetic Algorithm with the Real-Time Recurrent Learning algorithm to perform the fuzzy grammatical inference. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0893-6080(00)00081-2 | Neural Networks |
Keywords | Field | DocType |
training algorithms,fuzzy finite-state automaton,fuzzy recurrent neural network,training recurrent neural network,real-coded genetic algorithm,fuzzy grammatical inference,recurrent neural network,finite state automaton,genetic algorithm,grammatical inference,feedforward neural network | Intelligent control,Feedforward neural network,Grammar induction,Computer science,Recurrent neural network,Types of artificial neural networks,Time delay neural network,Artificial intelligence,Deep learning,Genetic algorithm,Machine learning | Journal |
Volume | Issue | ISSN |
14 | 1 | 0893-6080 |
Citations | PageRank | References |
57 | 3.18 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Blanco | 1 | 141 | 11.03 |
| M Delgado | 2 | 57 | 3.18 |
| M C Pegalajar | 3 | 154 | 12.01 |