一种基于QPSO的脉冲耦合神经网络参数的自适应确定方法

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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.

Key words： Pulse Coupled Neural Network (PCNN) Quantum-Behaved Particle Swarm Optimization (QPSO) Self-Adaptive Image Segmentation Mutual Information

Received: 12 October 2011

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	Articles by authors
	XU Xin-Zheng
	DING Shi-Fei
	SHI Zhong-Zhi
	ZHAO Zuo-Peng
	ZHU Hong

Cite this article:

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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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2012/V25/I6/909

[1] Eckhorn R.Neural Mechanisms of Scene Segmentation: Recordings from the Visual Cortex Suggest Basic Circuits or Linking Field Models.IEEE Trans on Neural Networks,1999,10(3): 464-479
[2] Miao Qiguang,Wang Baoshu.A Novel Image Fusion Algorithm Based on Local Contrast and Adaptive PCNN.Chinese Journal of Computers,2008,31(5): 875-880(in Chinese)
(苗启广,王宝树.基于局部对比度的自适应PCNN图像融合.计算机学报,2008,31(5): 875-880)
[3] Ma Yide,Dai Ruolan,Li Lian.Automated Image Segmentation Using Pulse Coupled Neural Networks and Images Entropy.Journal of China Institute of Communications,2002,23(1): 46-51 (in Chinese)
(马义德,戴若兰,李廉.一种基于脉冲耦合神经网络和图像熵的自动图像分割方法.通信学报,2002,23(1): 46-51)
[4] Lu Yunfeng,Miao Jun,Duan Lijuan,et al.A New Approach to Image Segmentation Based on Simplified Region Growing PCNN.Applied Mathematics and Computation,2008,205(2): 807-814
[5] Cheng Dansong,Liu Xiaofang,Tang Xianglong,et al.Image Segmentation Using Neighborhood Inspiring Pulse Coupled Neural Network.Journal of Huazhong University of Science and Technology: Nature Science Edition,2009,37(5): 33-37 (in Chinese)
(程丹松,刘晓芳,唐降龙,等.基于邻域激励脉冲耦合神经网络的图像分割.华中科技大学学报:自然科学版,2009,37(5): 33-37)
[6] Zhang Yudong,Wu Lenan.Image Denoising Based on SPCNN and Nagao.Science in China: Series F,2009,39(6): 598-607 (in Chinese)
(张煜东,吴乐南.基于SPCNN和Nagao滤波的图像去噪.中国科学:F辑,2009,39(6): 598-607)
[7] Ma Yide,Qi Chunliang,Qian Zhibai,et al.A Novel Image Compression Coding Algorithm Based on Pulse-Coupled Neural Network and Gram-Schmidt Orthogonal Base.Acta Electronica Sinica,2006,34(7): 1255-1259 (in Chinese)
(马义德,齐春亮,钱志柏,等.基于脉冲耦合神经网络和施密特正交基的一种新型图像压缩编码算法.电子学报,2006,34(7): 1255-1259)
[8] Gu Xiaodong,Guo Shide,Yu Daoheng.A New Approach for Image Shadow Processing Based on PCNN.Journal of Electronics and Information Technology,2004,26(3): 479-483 (in Chinese)
(顾晓东,郭仕德,余道衡.基于PCNN的图像阴影处理新方法.电子与信息学报,2004,26 (3): 479-483)
[9] Yu Jiangbo,Chen Houjin,Wang Wei,et al.Parameter Determination of Pulse Coupled Neural Network in Image Processing.Acta Electronica Sinica,2008,36(1): 81-85 (in Chinese)
(于江波,陈后金,王巍,等.脉冲耦合神经网络在图像处理中的参数确定.电子学报,2008,36(1): 81-85)
[10] Zhao Shijiang,Zhang Tianwen,Zhang Zhihong.A Study of a New Image Segmentation Algorithm Based on PCNN.Acta Electronica Sinica,2005,33(7): 1342-1344 (in Chinese)
(赵峙江,张田文,张志宏.一种新的基于PCNN的图像自动分割算法研究.电子学报,2005,33(7): 1342-1344)
[11] Ma Yide,Qi Chunliang.Study of Automated PCNN System Based on Genetic Algorithm.Journal of System Simulation,2006,18(3): 722-725 (in Chinese)
(马义德,齐春亮.基于遗传算法的脉冲耦合神经网络自动系统的研究.系统仿真学报,2006,18(3): 722-725)
[12] Bi Yingwei,Qiu Tianshuang.An Adaptive Image Segmentation Method Based on a Simplified PCNN.Acta Electronica Sinica,2005,33(4): 647-650 (in Chinese)
(毕英伟,邱天爽.一种基于简化PCNN的自适应图像分割方法.电子学报,2005,33(4): 647-650)
[13] Sun Jun.Particle Swarm Optimization with Particles Having Quantum Behavior.Ph.D Dissertation.Wuxi,China: Jiangnan University,2009 (in Chinese)
(孙俊.量子行为粒子群优化算法研究.博士学位论文.无锡:江南大学,2009)
[14] Shi Zhongzhi.Neural Networks.Beijing,China: Higher Education Press,2009 (in Chinese)
(史忠植.神经网络.北京:高等教育出版社,2009)
[15] Kennedy J,Eberhart R.Particle Swarm Optimization // Proc of the IEEE International Conference on Neural Network.Perth,Australia,1995,IV: 1942-1948
[16] Shi Y,Eberhart R.A Modified Particle Swarm Optimizer // Proc of the IEEE International Conference on Evolutionary Computation.Piscataway,USA,1998: 69-73
[17] Sun Jun,Feng Bin,Xu Wenbo.Particle Swarm Optimization with Particles Having Quantum Behavior // Proc of the Congress on Evolutionary Computation.Portland,USA,2004,I: 325-331
[18] Xi Maolong,Sun Jun,Wu Yong.Quantum-Behaved Particle Swarm Optimization with Binary Encoding.Control and Decision,2010,25(1): 99-104 (in Chinese)
(奚茂龙,孙俊,吴勇.一种二进制编码的量子粒子群优化算法.控制与决策,2010,25(1): 99-104)
[19] Otsu N.A Threshold Selection Method from Gray Level Histograms.IEEE Trans on Systems,Man and Cybernetics,1979,9(1): 62-66
[20] Ma Yide,Li Lian,Zhan Gun,et al.Pulse Coupled Neural Network and Digital Image Processing.Beijing,China: Science Press,2008 (in Chinese)
(马义德,李廉,绽绲,等.脉冲耦合神经网络与数字图像处理.北京:科学出版社,2008)
[21] Ma Jianhua,Chen Wufan,Huang Jing,et al.Metal Artifact Reduction in CT Based on Maximized the Difference of Mutual Information Segmentation.Acta Electronica Sinica,2009,37(8): 1779-1783 (in Chinese)
(马建华,陈武凡,黄静,等.基于最大互信息量熵差分割的CT金属伪影消除.电子学报,2009,37(8): 1779-1783)