Memristive Fuzzy Deep Learning Systems

As a novel nanoscale device, the memristor has elicited widespread interest in implementing compact and efficient neurocomputing systems in the hardware. In this article, fuzzy deep learning systems using fuzzy memristive modeling methods are presented. One key issue in memristive modeling is the device variation issue due to device-to-device and cycle-to-cycle variations. It is very difficult to distinguish the intermediate memristive states in a multilevel memristor. A fuzzy modeling method is therefore proposed to define the memristive states in a dynamic way. In addition, fuzzy deep learning systems are presented for fuzzy pattern recognition such as image recognition. To the authors' best knowledge, this is the first work utilizing memristive fuzzy deep learning systems to realize image recognition. The expected input and output in the memristive fuzzy deep learning systems are refined via a bidirectional fuzzy rule. The effectiveness of the proposed fuzzy methods has been verified with comprehensive methods, such as single-layer neural networks, multi-layer neural networks, convolutional neural networks, and $k$-nearest neighbor method. Another highlight of the proposed fuzzy deep learning system is that there is a great reduction in memory and a significant increase in the speed for image recognition tasks with the same level of testing accuracy.