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摘要
This paper introduces the establishment of a predicting model for normalized traffic accident proneness (NAP) after drinking. Eighteen drivers' EEGs and traffic accident proneness were measured respectively in different drunken states. Considering the instant complexity and long-term periodicity of EEGs measured from the left frontal lobe, the power gain of δ wave and fuzzy entropy of EEG were invented and calculated. A hybrid Sigma-Pi neural network was introduced and studied to help building the predict model for NAP from the aspects of both power gain of δ wave and fuzzy entropy of EEG. Experiments proved that the predicted values of NAP accord with the actual values, with a consistent increase and decrease characteristic. When the amount of the alcohol drunk is less than 50 of the subjective maximum alcohol to drink, the errors were very small, but when the amount of the alcohol drunk is more than 50, the errors became bigger along with the increase volume of alcohol to drink.
