中文摘要：

针对目前脉冲耦合神经网络（PCNN）神经元模型中的参数主要通过人工定义的问题，提出一种基于量子微粒群优化（QPSO）算法的PCNN参数自动确定方法，并分析该算法的时间复杂度．该方法利用PCNN分割后的图像熵作为QPSO算法的适应度函数，在解空间中自动搜索PCNN中待确定参数的最优值，提供一种PCNN神经元模型中的参数自动确定方法．将该方法应用于图像分割时，以互信息量作为图像分割评价标准．仿真结果表明文中方法实现正确的图像分割，其性能优于Otsu方法、人工调整PCNN参数方法、遗传算法优化方法和微粒群优化方法，表现出较好的鲁棒性．

英文摘要：

Considering the parameters of pulse coupled neural network （PCNN） are mainly defined manually, a method based on quantum-behaved particle swarm optimization （QPSO） is presented to automatically determine the parameters in the neuron model of PCNN. Meanwhile, the time complexity of the proposed algorithm is analyzed. In proposed method, QPSO algorithm is used to automatically search the optimum values of parameters of the PCNN model or its simplified models in the solution space when the entropy of the image is defined as the fitness function of QPSO algorithm. The simulation results of image segmentation show that the proposed method obtains correct segmentation of Lena image. When mutual information （MI） is used as evaluation criteria, the performance of the proposed method is better than that of other methods, such as Otsu method, manual adjustment method of PCNN parameters, genetic algorithm and PSO.