Models for performance prediction

Biinz, A.P., Braun, B. and Janowsky, R. (1998) Application of quantitative structure-performance relationship and neural network models for the prediction of physical properties from molecular structure. Ind. Eng. Chem. Res., 37, 3044—3051. 

CREAM Cognitive reliability and error analysis method. In CREAM, the operator model is more significant and less simplistic than that of first generation approaches. It can be used both for performance prediction as well as accident analysis. CREAM is used for evaluation of the probability of a human error for completion of a specific task. There is good application of fuzzy logic in this method. It was again started for nuclear application but has wider applications, too. 

Tetteh and co-workers described the application of radial basis function (RBF) neural network models for property prediction and screening (114). They employed a network optimization strategy based on biharmonic spline interpolation for the selection of an optimum number of RBF neurons in the hidden layer and their associated spread parameter. Comparisons with the performance of a PLS regression model showed the superior predictive ability of the RBF neural model. 

Multiple linear regression analysis is a widely used method, in this case assuming that a linear relationship exists between solubility and the 18 input variables. The multilinear regression analy.si.s was performed by the SPSS program [30]. The training set was used to build a model, and the test set was used for the prediction of solubility. The MLRA model provided, for the training set, a correlation coefficient r = 0.92 and a standard deviation of, s = 0,78, and for the test set, r = 0.94 and s = 0.68. 

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