Dangerous driving behaviors are diverse and complex. Determining how to analyze the behavior of public drivers objectively accurately has always been a research challenge. This proposes macroscopic dynamic method for evaluating drivers’ dangerous degree based on fuzzy inference system. It also designs fuzzy-macro long short-term memory (LSTM), variant LSTM recurrent neural networks, which can p...

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the anteceden...

A reliable multi-step predictor is very useful to a wide array of applications to forecast the behavior of dynamic systems. The objective of this paper is to develop a more robust data-driven predictor for time series forecasting. Based on simulation analysis, it is found that multi-step-ahead forecasting schemes based on step inputs perform better than those based on sequential inputs. It is a...