%0 Conference Paper %1 angeline:1998:spie %A Angeline, Peter J. %B Proceedings of SPIE: Application and Science of Computational Intelligence %D 1998 %E Rogers, S. %E Fogel, D. %E Bezdek, J. %E Bosacchi, B. %K algorithms, chaotic computation, evolutionary genetic networks, neural prediction programming, series time %P 170--80 %T Evolving Predictors for Chaotic Time Series %U http://www.natural-selection.com/Library/1998/spie98.pdf %V 3390 %X Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods performance is shown to compare favorably with using larger neural networks.

@inproceedings{angeline:1998:spie, abstract = {Neural networks are a popular representation for inducing single-step predictors for chaotic times series. For complex time series it is often the case that a large number of hidden units must be used to reliably acquire appropriate predictors. This paper describes an evolutionary method that evolves a class of dynamic systems with a form similar to neural networks but requiring fewer computational units. Results for experiments on two popular chaotic times series are described and the current methods performance is shown to compare favorably with using larger neural networks.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Angeline, Peter J.}, biburl = {https://www.bibsonomy.org/bibtex/28ab8341138617aa695a5e72ade431127/brazovayeye}, booktitle = {Proceedings of SPIE: Application and Science of Computational Intelligence}, editor = {Rogers, S. and Fogel, D. and Bezdek, J. and Bosacchi, B.}, interhash = {d1b2226a0fef2655ce809ea8ae7410d5}, intrahash = {8ab8341138617aa695a5e72ade431127}, keywords = {algorithms, chaotic computation, evolutionary genetic networks, neural prediction programming, series time}, organisation = {SPIE}, pages = {170--80}, publisher_address = {Bellingham, WA, USA}, size = {11 pages}, timestamp = {2008-06-19T17:35:47.000+0200}, title = {Evolving Predictors for Chaotic Time Series}, url = {http://www.natural-selection.com/Library/1998/spie98.pdf}, volume = 3390, year = 1998 }