A Self-Adaptive Method for Optimizing the Parameters of Pulse Coupled Neural Network Based on QPSO Algorithm
XU Xin-Zheng1,2, DING Shi-Fei1,2, SHI Zhong-Zhi2, ZHAO Zuo-Peng1, ZHU Hong1
1.School of Computer Science and Technology,China University of Mining and Technology,Xuzhou 221116 2.Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190
Abstract 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.
XU Xin-Zheng,DING Shi-Fei,SHI Zhong-Zhi等. A Self-Adaptive Method for Optimizing the Parameters of Pulse Coupled Neural Network Based on QPSO Algorithm[J]. , 2012, 25(6): 909-915.
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2012, Vol. 25 
