(Received: March 8, 2001; Accepted for publication: October 10, 2001; Published on Web: March 22, 2002)
For molecular design of freon alternatives, we attempted to extract bond parameter
characterizing the infrared absorption intensity in the 1500-500 cm-1
region from 44 kinds of fluorine-containing molecules: freon alternatives in
gas phase by the analysis of sensitivity and differential coefficients of input
parameter for the three layers perceptron type neural network. The analysis
of differential coefficients of input parameter for the three layers perceptron
type neural network was developed by Aoyama and Ichakawa[3,
4] and was newly equipped into a neural network
simulator Neco [2, 5,
8 - 13].
The importance of 8 bond types: C-C, C=C, C-O, C=O, C-H, C-F, C-Cl, O-H were examined using a well educated neural network, where the error of leave-one out test is less than 0.007. The error is acceptable because the value corresponds almost 5% error of intensity and 10% error is usually included in the observed values.
The numbers of C=O and O-H bonds increase the intensity whereas C-O and C-F have less effect. In Figure 2, the sensitivity analysis suggested that the number of C-C bonds is to be unimportant for the intensity. However, the result of the differential coefficients analysis suggested the importance of the number of C-C bonds as shown in Figure 3.
The results of the sensitivity analysis and the differential coefficients suggested that ether type freon alternatives have relatively small infrared absorption intensity in the 1500-500 cm-1 region.
Keywords: Freon Alternatives, Infrared Absorption Intensity, Molecular Design, Sensitivity Analysis, Differential Coefficients Analysis, Neural Network
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