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Genetic training of network using chaos concept: Application to QSAR studies of vibration modes of tetrahedral halides
Author(s) -
Lu Qingzhang,
Shen Guoli,
Yu Ruqin
Publication year - 2002
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.10149
Subject(s) - tetrahedron , quantitative structure–activity relationship , halide , training (meteorology) , chaos (operating system) , vibration , computer science , genetic algorithm , computational chemistry , chemistry , physics , machine learning , crystallography , inorganic chemistry , acoustics , computer security , meteorology
The chaotic dynamical system is introduced ingenetic algorithm to train ANN to formulate the CGANN algorithm. Logistic mapping as one of the most important chaotic dynamic mappings provides each new generation a high chance to hold GA's population diversity. This enhances the ability to overcome overfitting in training an ANN. The proposed CGANN has been used for QSAR studies to predict the tetrahedral modes (ν 1 (A1) and ν 2 (E)) of halides [MX 4 ] ε . The frequencies predicted by QSAR were compared with those calculated by quantum chemistry methods including PM3, AM1, and MNDO/d. The possibility of improving the predictive ability of QSAR by including quantum chemistry parameters as feature variables has been investigated using tetrahedral tetrahalide examples. © 2002 Wiley Periodicals, Inc. J Comput Chem 14: 1357–1365, 2002