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Abstract

The application of artificial neural network (ANN) to formulation design of the natural color, halogen-free flame retardant materials was conducted by means of flame retardant expert system (FRES 2.0). This work involves an ANN model with three objects. The optimized outputs, such as LOI, TS and EL were shown to be in good accordance with experiments. An intuitive graphical analysis led to dealing with the correlation between performance and components in formulations, where normally subjected to serious nonlinearity and/or multiobjects. It should be emphasized that together with the three-layered BP (back-propagation) network-based modeling, one can do what can hardly be done before with conventional techniques.

Keywords

artificial neural network halogen-free flame retardant formulation design FRES 2.0

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References

Simpson, P. K. Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations, Pergamon Press: New York,1990.

J. Zupan and J. Gasteiger , Neural Networks for Chemists: An Introduction, edited by J. Zupan and J. Gasteiger , Weinheim, VCH (Germany), 1993.

J. Gasteiger and J. Zupan , “Neural Networks in Chemistry,” Angew. Chem., Int. Ed. Engl. 1993, 32, 503-527.

Rose, V. S. , Croall, I. F. and MacFie, H. J.

“An Application of Unsupervised Neural Network Methodology (Kohonen-Toplogy Preserving Mapping) to QSAR Analysis,”

Quant. Struct.-Act. Relat., 1991, 10, 6-15.

Chastrette, M. and de Saint Laumer, J. Y.

“Structure-order Relationships Using Neural Networks,”

Eur. J. Med. Chem., 1991, 26, 829-833.

Weinstein, J. N. , Kohn, K. W. , Grever, M. R. , et al . “Neural Computing in Cancer Drug Development: Predicting Mechanisms of Action,” Science, 1992, 258, 447-451.

Tetko, I. V. , Luik, A. I. and Poda, G. I.

“Application of Neural Networks in Structure-Activity Relationships of Small Number of Molecules,”

J. Med. Chem., 1993, 36, 811-814.

Takahiro S. and Masaru I. , “Neural Networks Techniques Applied to Predict Flammability Limits of Organic Compounds,” Fire and Materials, 1995, 19, 179-189.

Juntao, X. , Chongwei, X. , Jianqi, W. and Congming, H. , “The Application of Artificial Neural Network (ANN) Technique to Formulation Design of Flame Retardant Polymers—I. Intumescent Flame Retardant PA-66 Composites,” Journal of Fire Sciences, 2001: 19(1).

10.

Hecht-Nielsen, R. , “Theory of the Back Propagation Neural Network,” Proc. of IJCNN, 1989, 1, 593-603.

11.

Juntao, X. , Jianqi, W. and Congming, H. , “Expert System FRES 2.0 Applied to Design Flame Retardant Polymeric Composites,” Computers and Applied Chemistry (in chinese) 2000:17(6).

12.

Kaitai, F. , Homogeneous Design and Homogeneous Design Tables, Beijing: Science Press, 1994.

13.

Rao, S. S. , Optimization Theory and Applications, Wiley Eastern Limited, 1984.

The Application of Artificial Neural Network (ANN) Technique to Formulation Design of Flame Retardant Polymers—II. Halogen-Free Flame Retardant Polymeric Composites

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