G.Pang,K.Takahashi等人基于模糊神经网络方法对动态路径

G.Pang,K.Takahashi等人基于模糊神经网络方法对动态路径选择系统中的自适应路径选择进了研究，通过模糊神经网络对驾驶员的路径选择进行训练，建立驾驶员路径选择函数，该函数具有自适应性[15]。GAO Feng利用仿真方法，对驾驶员路径选择行为进行建模，该模型以决策论和贝叶斯理论为基础，仿真结果表明决策论和贝叶斯理论可以有利的说明驾驶员动态路径选择行为[16]。

結果 (英文) 1:

G.Pang,K.Takahashi and others based on fuzzy neural network method for dynamic adaptive routing in the routing system into the study by fuzzy neural network to train driver's route choice, establishing drivers ' route selection function, the function is adaptive [15]. GAO Feng by using simulation methods on driver's route choice behavior model, the model based on decision theory and Bayesian theory, simulation results show that the description of decision theory and Bayesian inference will be conducive to dynamic route choice behavior [16].

結果 (英文) 2:

G.Pang, K.Takahashi et al method based on fuzzy neural network system for dynamic adaptive routing path selection into the study, fuzzy neural network paths through the driver's selection for training, the establishment of the driver path selection function, which function is adaptive [15]. GAO Feng using simulation methods, the driver route choice behavior modeling, the model and Bayesian decision theory to theory, simulation results show that the Bayesian decision theory and the theory can be beneficial to the driver dynamic route choice behavior described [ 16].

結果 (英文) 3:

G.Pang, K.Takahashi et al. Fuzzy neural network method into the research of dynamic routing path selection based on adaptive system, through the path of the driver fuzzy neural network selection for training, establish driver path selection function, the function is adaptive [15]. Using GAO Feng simulation method, the modeling of driver's route choice behavior, this model is based on decision theory and Bias's theory, the simulation results show that the decision theory and Bias theory can be advantageous that drivers dynamic route choice behavior of [16].