彩色空间信息更加有利于最佳特征向量的提取，为了获得彩色空间中脉冲耦合神经网络(PCNN)参数，通过模拟视觉感知系统感知彩色信息的过程，利用PCNN分别在RGB模型和HSV模型的颜色空间中求取熵序列作为分类特征，通过实验选取最佳PCNN参数。针对国家标准彩色道路交通标志图像库GB5768-1999中43个警告标志，42个禁令标志，29个指示标志进行实验，结果表明，参数为αL=1,αF=0.1,αE=1,VL=0.2,VF=0.5,VE=27,β=0.1，选取N=50时，蓝色分量中所得的熵序列大类分类效果最佳。RGB模型中蓝色分量能够充分反应交通标志的彩色分类信息，所提取的熵序列向量能够明显区分三个大类，优于传统将彩色图像转换成灰度图像的处理方法。

Optimal feature vector can be extracted in color image space. To acquire optimal parameters of pulse coupled neural network (PCNN) for color image space, color image processing are carried out by simulating visual perception of mammal. Entropy sequence of PCNN is calculated as feature vector to classify the traffic signs in RGB and HSV color space. Optimal parameters of PCNN are acquired through experiments. Experiments are carried out in GB5768-1999 of standard traffic signs image database. Experimental results show that the maximum inter-class distance value is acquired among 43 warning signs, 42 prohibition signs, and 29 instruction signs based on the entropy sequence of PCNN in blue color space while the optimal parameters of PCNN are αL=1, αF=0.1, αE=1, VL=0.2, VF=0.5, VE=27, β=0.1, and N=50 iterations. Blue component of the RGB model can fully reflect the color characteristics of traffic signs, entropy sequence of PCNN can be used as the vector sequences to distinguish three categories, superior to conventional color image into a grayscale image processing methods.