This paper concerns the training of a single-layer morphological perceptron using disciplined convex-concave programming (DCCP). We introduce an algorithm referred to as K-DDCCP, which combines the existing single-layer morphological perceptron (SLMP) model proposed by Ritter and Urcid with the weighted disciplined convex-concave programming (WDCCP) algorithm by Charisopoulos and Maragos. The proposed training algorithm leverages the disciplined convex-concave procedure (DCCP) and formulates a non-convex optimization problem for binary classification. To tackle this problem, the constraints are expressed as differences of convex functions, enabling the application of the DCCP package. The experimental results confirm the effectiveness of the K-DDCCP algorithm in solving binary classification problems. Overall, this work contributes to the field of morphological neural networks by proposing an algorithm that extends the capabilities of the SLMP model.

翻译：本文关注使用规整凸凹规划(DCCP)训练单层形态感知器的问题。我们提出了一种名为K-DDCCP的算法，该算法将Ritter和Urcid提出的现有单层形态感知器(SLMP)模型与Charisopoulos和Maragos提出的加权规整凸凹规划(WDCCP)算法相结合。所提出的训练算法利用规整凸凹过程(DCCP)，为二分类问题建立了一个非凸优化模型。为了解决该问题，我们将约束条件表示为凸函数的差值，从而能够应用DCCP软件包。实验结果证实了K-DDCCP算法在解决二分类问题中的有效性。总体而言，本研究通过提出一种扩展SLMP模型能力的算法，为形态神经网络领域做出了贡献。