基于蚁群算法的模糊分类系统设计

提出了一种基于最大-最小蚁群算法的模糊分类系统设计方法.该方法通过两个阶段来实现:特征变量选择和模型参数优化.首先采用蚁群算法对特征变量进行选择,得到一组具有较高分辩性能的特征变量,提高模型的解释性;在模型结构确定后,蚁群算法从训练样本中提取信息对模型的参数进行优化,在保证模型精确性的前提下,构造具有较少变量数目及规则数目的模糊模型,实现了精确性与解释性的折衷.最后将本方法运用到Iris和Wine数据样本分类问题中,并将结果与其它方法进行比较,仿真结果证明了该方法的有效性.     

基于模糊穴的轨道交通安全评估方法的研究及应用

提出一种基于模糊穴的轨道交通安全评估方法FCRS（Fuzzy-Cell based Rail traffic Safety analysis）。该方法从轨道交通安全相关要素的辨识，内外部属性的动态演化过程，分析轨道交通安全状态的动态演变机理。利用神经网络实现了模糊穴模型。实验及应用表明该方法在改善主动安全预警水平中取得了较好的效果。     

Effect of Aspiration and Mean Gain on the Emergence of Cooperation in Unidirectional Pedestrian Flow

When more than one pedestrian want to move to the same site,conflicts appear and thus the involved pedestrians play a motion game.In order to describe the emergence of cooperation during the conflict resolving process,an evolutionary cellular automation model is established considering the effect of aspiration and mean gain.In each game,pedestrian may be gentle cooperator or aggressive defector.We propose a set of win-stay-lose-shrift WSLS like rules for updating pedestrian's strategy.These rules prescribe that if the mean gain of current strategy between some given steps is larger than aspiration the strategy keeps,otherwise the strategy changes.The simulation results show that a high level aspiration will lead to more cooperation.With the increment of the statistic length,pedestrians will be more rational in decision making.It is also found that when the aspiration level is small enough and the statistic length is large enough all the pedestrian will turn to defectors.We use the prisoner's dilemma model to explain it.At last we discuss the effect of aspiration on fundamental diagram.