论文标题
图像分类的深度神经模糊网络
A Deep Neuro-Fuzzy Network for Image Classification
论文作者
论文摘要
神经网络和模糊系统与神经模糊系统的结合将模糊推理规则集成到连接式网络中。但是，现有的神经模糊系统是在具有较低概括能力较低的浅结构下开发的。我们提出了第一个端到端的深神经模糊网络，并研究了其用于图像分类的应用。基于高加a-sugeno-kang（TSK）模型模型的定义，即模型模糊的推理操作和模糊池操作，开发了两个新操作；这些操作的堆栈包括该网络中的层。我们评估了MNIST，CIFAR-10和CIFAR-100数据集的网络，发现该网络在这些基准测试中具有合理的精度。
The combination of neural network and fuzzy systems into neuro-fuzzy systems integrates fuzzy reasoning rules into the connectionist networks. However, the existing neuro-fuzzy systems are developed under shallow structures having lower generalization capacity. We propose the first end-to-end deep neuro-fuzzy network and investigate its application for image classification. Two new operations are developed based on definitions of Takagi-Sugeno-Kang (TSK) fuzzy model namely fuzzy inference operation and fuzzy pooling operations; stacks of these operations comprise the layers in this network. We evaluate the network on MNIST, CIFAR-10 and CIFAR-100 datasets, finding that the network has a reasonable accuracy in these benchmarks.
