Quantum particle swarm optimisation based on chaotic mutation for automatic parameters determination of pulse coupled neural network

  • Authors:

  • Linlin Mu;Maozheng Zhao;Chaozhu Zhang

  • Affiliations:

  • College of Information and Communication Engineering, Harbin Engineering University, No. 145, Nantong Street, Nangang District, Harbin, Heilongjiang Province, 150001, China;College of Information and Communication Engineering, Harbin Engineering University, No. 145, Nantong Street, Nangang District, Harbin, Heilongjiang Province, 150001, China;College of Information and Communication Engineering, Harbin Engineering University, No. 145, Nantong Street, Nangang District, Harbin, Heilongjiang Province, 150001, China

  • Venue:
  • International Journal of Computing Science and Mathematics
  • Year:
  • 2013

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Abstract

Pulse coupled neural network PCNN, a well-known class of neural networks, has original advantages when applied to image segmentation because of its biological background. However, when PCNN is used, the main problem is that its parameters are not self-adapting according to different image, which limits the application range of PCNN. Considering that, this paper proposed a new method based on quantum particle swarm optimisation QPSO and chaotic mutation to determine automatically the parameters of PCNN. In this method, the chaotic mutation-quantum particle swarm optimisation CM-QPSO is used to search automatically the optimal solution of the solution space of PCNN's parameters for image segmentation. Simulation results demonstrate that the proposed method is accurate and robust for image segmentation, and its performance is superior to the methods of GA and PSO when Shannon entropy is adopted as evaluation criteria.