RT Book, Section
T1 Design of artificial neural networks based on genetic algorithms to forecast time series
A1 Peralta, Juan
A1 Gutiérrez Sánchez, Germán
A1 Sanchis de Miguel, María Araceli
AB In this work an initial approach to design Artificial Neural Networks to forecast time series is tackle, and the automatic process to design is carried out by a Genetic Algorithm. A key issue for these kinds of approaches is what information is included in the chromosome that represents an Artificial Neural Network. There are two principal ideas about this question: first, the chromosome contains information about parameters of the topology, architecture, learning parameters, etc. of the Artificial Neural Network, i.e. Direct Encoding Scheme; second, the chromosome contains the necessary information so that a constructive method gives rise to an Artificial Neural Network topology (or architecture), i.e. Indirect Encoding Scheme. The results for a Direct Encoding Scheme (in order to compare with Indirect Encoding Schemes developed in future works) to design Artificial Neural Networks for NN3 Forecasting Time Series Competition are shown.
PB Springer
SN 978-3-540-74971-4
SN 1615-3871 (impreso)
SN 1860-0794 (online)
YR 2007
FD 2007
LK https://hdl.handle.net/10016/9783
UL https://hdl.handle.net/10016/9783
LA eng
DS e-Archivo
RD 1 may. 2024