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Memetic evolutionary training for recurrent neural networks: an application to time‐series prediction
Author(s) -
Delgado M.,
Pegalajar M.C.,
Cuéllar M.P.
Publication year - 2006
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2006.00327.x
Subject(s) - computer science , memetic algorithm , artificial neural network , recurrent neural network , artificial intelligence , evolutionary algorithm , evolutionary acquisition of neural topologies , machine learning , types of artificial neural networks , series (stratigraphy) , time delay neural network , paleontology , biology
Artificial neural networks are bio‐inspired mathematical models that have been widely used to solve complex problems. The training of a neural network is an important issue to deal with, since traditional gradient‐based algorithms become easily trapped in local optimal solutions, therefore increasing the time taken in the experimental step. This problem is greater in recurrent neural networks, where the gradient propagation across the recurrence makes the training difficult for long‐term dependences. On the other hand, evolutionary algorithms are search and optimization techniques which have been proved to solve many problems effectively. In the case of recurrent neural networks, the training using evolutionary algorithms has provided promising results. In this work, we propose two hybrid evolutionary algorithms as an alternative to improve the training of dynamic recurrent neural networks. The experimental section makes a comparative study of the algorithms proposed, to train Elman recurrent neural networks in time‐series prediction problems.