基于遗传算法的脉冲耦合神经网络自动系统的研究

Study of Automated PCNN System Based on Genetic Algorithm

Eckhorn等人提出的脉冲耦合神经网络(PulseCoupledNeuralNetwork,以下简称为PCNN)在图像处理中得到了十分广泛的应用,但是其多个参数的设置给实际应用造成了很大的困难。尤其是在图像分割中,不同类型的图像要求不同的分割参数,不同的参数对图像分割结果影响很大。而遗传算法具有对参数自动寻优的优势,为此,将其和PCNN相结合提出了一种基于遗传算法的PCNN自动系统的实现方案,并应用于图像分割。分割试验仿真结果验证了该自动系统方案的正确性和可信性,即不仅可以实现正确的图像分割,而且参数可以自动设置省去了人工试验的麻烦,同时分割速度也有所提高。

Pulse coupled neural network （PCNN） based on Eckhom＇s model finds many applications in image processing. Because the parameters greatly affect the performance of PCNN, finding the optimal parameters becomes an onerous task.. Especially in image segmentation, the parameters vary with the image that needs to process. An automated PCNN system was proposed that banded PCNN and genetic algorithm together and it was used to segment the image automatically and successfully. The correctness and dependability of the automated PCNN system are verified by experiment results, that is to say, the quality of the segmentation based on the automated PCNN system is much better and time-consuming and parameters-setting automatically is the main feature of the system.