Created by W.Langdon from gp-bibliography.bib Revision:1.4549

@Article{Bagheri:2015:SAR_QSAR_ER, author = "M. Bagheri and T. N. G. Borhani and A. H. Gandomi and Z. A. Manan", title = "A simple modelling approach for prediction of standard state real gas entropy of pure materials", journal = "SAR and QSAR in Environmental Research", year = "2014", volume = "25", number = "9", pages = "695--710", keywords = "genetic algorithms, genetic programming, linear genetic programming (LGP), standard state absolute entropy of real gases (SSTD), feed forward neural network (FFNN), quantitative structure entropy relationship, exergy analysis", URL = "http://www.tandfonline.com/doi/abs/10.1080/1062936X.2014.942356", URL = "http://www.tandfonline.com/doi/full/10.1080/1062936X.2014.942356", DOI = "doi:10.1080/1062936X.2014.942356", abstract = "The performance of an energy conversion system depends on exergy analysis and entropy generation minimisation. A new simple four-parameter equation is presented in this paper to predict the standard state absolute entropy of real gases (SSTD). The model development and validation were accomplished using the Linear Genetic Programming (LGP) method and a comprehensive dataset of 1727 widely used materials. The proposed model was compared with the results obtained using a three-layer feed forward neural network model (FFNN model). The root-mean-square error (RMSE) and the coefficient of determination (r2) of all data obtained for the LGP model were 52.24 J/(mol K) and 0.885, respectively. Several statistical assessments were used to evaluate the predictive power of the model. In addition, this study provides an appropriate understanding of the most important molecular variables for exergy analysis. Compared with the LGP based model, the application of FFNN improved the r-squared to 0.914. The developed model is useful in the design of materials to achieve a desired entropy value.", }

Genetic Programming entries for Mehdi Bagheri Tohid Nejad Ghaffar Borhani A H Gandomi Zainuddin Abdul Manan