【作者】 齐春亮；

【导师】 马义德；

【作者基本信息】 兰州大学 ， 电路与系统， 2006， 硕士

【摘要】 人工神经网络在一定程度上模仿了生物神经系统的智慧和功能,在图像处理和人工智能领域有着极其重要的地位。而脉冲耦合神经网络作为新型神经网络模型,自从90年代被Johnson提出以来,一直是国内外众多学者的研究热点。由于脉冲耦合神经网络是对猫的大脑视觉皮层进行研究而发展起来的,与传统神经网络相比有其独特特性:神经元之间的乘积耦合和动态脉冲发放特性,这些特性使其在图像处理领域得到了非常广泛的应用。 但是在模型中众多参数的设定限制了其进一步的应用,所以在很多文献中,均采用了简化的模型。在简化模型中,虽然参数数量大为减少,可是关键参数仍然根据具体应用通过试验来手工设定或者使用经验值,在一定程度上影响了试验结果的精度,给后续的工作造成了一定的障碍。 到目前为止,无人对脉冲耦合神经网络的模型参数设置进行全面和系统的研究。针对上述情况,本文提出了两种算法来对脉冲耦合神经网络模型参数进行自动设置。一是利用遗传算法对参数的自动全局寻优能力,将遗传算法和脉冲耦合神经网络模型进行结合,来完成脉冲耦合神经网络网络参数的自动设置,在此基础上研制了脉冲耦合神经网络自动系统,通过对图像分割进行了自动系统的性能检验。第二种方法是根据LMS准则,采用梯度下降法来对模型的参数进行自动搜索,逐步缩小期望输出和实际输出的误差,从而在训练结束时候得到参数准最优值,建立了一阶自适应脉冲耦合神经网络。该系统对于给定的期望输出和实际输入,能够自动调节参数数值使实际输出接近期望输出。 试验结果验证了文中提出的两种算法和相应系统的可信性和正确性,为脉冲耦合神经网络模型的后续研究奠定了良好的基础。更多还原

【Abstract】 Artificial neural networks plays the key role in the fields of artificial intelligence and image processing because of it’s imitation of the wisdom and power of biological nervous system. As a new kind of neural network, Pulse-Coupled Neural Network (PCNN) has been put more emphasis on the research in many fields since it was proposed by Johnson in 1994. PCNN model was created by the research and understanding in the visual cortex of small mammal such as cat and produces synchronous bursts of pulses from neurons with similar activity, effectively grouping them by phase and pulse frequency, which makes it significantly different from the conventional artificial neurons. Just because the model derives directly from the studies of the mammal’s visual properties, PCNN has been used successfully in image processing fields.PCNN model is a algorithm with multiple parameters, and finding the proper value of these parameters is an onerous task. The drawback of the PCNN hinders its further development and application. So many simplified PCNN model is adopted in many papers. The remaining parameters are, nevertheless, manually adjusted according to the concrete application. The high accuracy is unachievable with the simplified model and the quality of latter study is affected.Up to now, no papers which investigate the auto-determination of the parameter values of PCNN model in full-range and systematically exist. Based on the mentioned above, two algorithms which can performs the auto-determination are presented in this research. As one part of this paper, genetic algorithm which is a general purpose stochastic optimization for search problem is utilized to determine these parameter values automatically, and an automatic PCNN system is established. Through the segmentation, the performance of the automatic PCNN system is verified. As another part of this paper, gradient descent algorithm is adopted to search parameters which can reduce the error between the desired output and the actual output gradually according to the LMS principle, then a self-adaptive PCNN system is established. Given only an input and the desired output, the adaptive PCNN system can find all parameter values necessary to approximate the desired output.The correctness and dependability of two algorithms are verified by experiment results, the good foundation for the latter study of PCNN model in image processing is established.更多还原