Continuous Quantum Goose Swarm Algorithm Evolving Pulse-Coupled Neural Network System Parameters for Automatic Image Segmentation
A technology of pulse-coupled neural and network systems, which is applied in the field of automatic image segmentation of continuous quantum goose swarm algorithm evolution pulse-coupled neural network system parameters, can solve the problems of insufficient scope of application, single entropy criterion for optimal parameter optimization goals, etc.
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[0123] The population size of particle swarm optimization (PSO), cultural algorithm (CA) and quantum goose swarm algorithm (QGSO) is 20, and the maximum number of iterations is 20 generations. For the parameters of the Quantum Goose Swarm Algorithm, refer to the parameter setting of the patent "A Specific Implementation Example of Image Segmentation". For other parameters of the Particle Swarm Algorithm, refer to Lu Guifu et al. 146) the PSO algorithm in "A PCNN Image Segmentation Algorithm for Automatic Parameter Optimization" published on the Internet; other parameters of the cultural algorithm refer to the master's thesis "Research on PCNN Parameter Calibration Based on Cultural Algorithm".
[0124] Now use a specific example to illustrate the specific steps of using the Quantum Goose Swarm Algorithm for PCNN image segmentation:
[0125] Step1: Set the population size M=20 and the maximum iteration number N=20, and set the weighted value of the combined weighted entropy to ...
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