A memristive fuzzy neural network with applications to classification task: A programmable circuit system.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41529447.
- Also identified by DOI 10.1016/j.neunet.2026.108547.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Inspired by fuzzy inference systems and neural networks, this paper presents the design of a memristive fuzzy neural network (M-FNN) with applications to classification tasks, implemented in a computing-in-memory (CIM) architecture. Specifically, a modified first-order T-S model is optimized for hardware deployment with a three-dimensional memristor crossbar array (3-D MCA). Based on this model, the overall M-FNN architecture is constructed, where time-controlled schedules enable continuous samples processing. A dedicated writing scheme and multiple functional modules are introduced, along with a peripheral circuit scheme to enhance programmability. Various classification experiments on multiple machine learning datasets demonstrate the adaptability of M-FNN. Error tolerance results further verify its robustness in analog computing. Compared with ASIC-based schemes, the proposed M-FNN achieves higher programmability, allowing flexible adjustment of inputs, rules, and outputs. Additionally, compared with programmable chips such as CPU and FPGA, the proposed M-FNN demonstrates significant improvements in inference speed, integrated area, and power consumption by factors of 1.35 × 10<sup>5</sup>, 914, and 33, respectively.