Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Bernal-Urbina:2008:ijcnn,

• author = "M. Bernal-Urbina and A. Flores-Mendez",
• title = "Time Series Forecasting through Polynomial Artificial Neural Networks and Genetic Programming",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "3325--3330",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-1821-3",
• file = "NN0903.pdf",
• DOI = "doi:10.1109/IJCNN.2008.4634270",
• ISSN = "1098-7576",
• abstract = "The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analysed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimisation algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.",
• keywords = "genetic algorithms, genetic programming",
• notes = "Also known as \cite{4634270}. WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Manuel Bernal-Urbina Alejandro Flores-Mendez

Citations