Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing
Evolutionary Computation Schemes based on Max Plus Algebra and Their Application to Image Processing
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基于最大加代数的进化计算方案及其在图像处理中的应用
DOI：
10.1109/ispacs.2006.364715
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发表时间：
2006
期刊：
影响因子：
--
通讯作者：
Chang
中科院分区：
文献类型：
--
作者：
H. Nobuhara;Chang
提出了一种基于混合遗传算法的形态神经网络学习方法。形态神经网络是基于极大极大代数的神经网络，采用带导数运算的学习方法很难对神经网络的系数进行优化。为了解决这一困难，提出了一种基于混合遗传算法的神经网络系数优化学习方法。通过对从标准图像库(SIDBA)中提取的测试图像进行的图像压缩/重建实验，证实了所提出的学习方法得到的重建图像的质量优于传统方法。
A hybrid genetic algorithm based learning method for the morphological neural networks (MNN) is proposed. The morphological neural networks are based on max-plus algebra, therefore, it is difficult to optimize the coefficients of MNN by the learning method with derivative operations. In order to solve the difficulty, a hybrid genetic algorithm based learning method to optimize the coefficients of MNN is proposed. Through the image compression/reconstruction experiment using test images extracted from standard image database (SIDBA), it is confirmed that the quality of the reconstructed images obtained by the proposed learning method is better than that obtained by the conventional method.