Bicomplex convolutional neural networks (BCCNN) are a natural extension of the quaternion convolutional neural networks for the bicomplex case. As it happens with the quaternionic case, BCCNN has the capability of learning and modelling external dependencies that exist between neighbour features of an input vector and internal latent dependencies within the feature. This property arises from the fact that, under certain circumstances, it is possible to deal with the bicomplex number in a component-wise way. In this paper, we present a BCCNN, and we apply it to a classification task involving the colourized version of the well-known dataset MNIST. Besides the novelty of considering bicomplex numbers, our CNN considers an activation function a Bessel-type function. As we see, our results present better results compared with the one where the classical ReLU activation function is considered.

双复数卷积神经网络 (BCCNN) 是双复数情况下四元数卷积神经网络的自然扩展。与四元数情况一样，BCCNN 具有学习和建模输入向量的相邻特征之间存在的外部依赖关系和特征内的内部潜在依赖关系的能力。此属性源于这样一个事实，即在某些情况下，可以以分量方式处理双复数。在本文中，我们提出了 BCCNN，并将其应用于涉及著名数据集 MNIST 彩色版本的分类任务。除了考虑双复数的新颖性外，我们的 CNN 还认为激活函数是贝塞尔函数。正如我们所见，