Memristor-Based Brain Emotional Learning Neural Network With Attention Mechanism and Its Application
Quanli Deng, Chunhua Wang, Yichuang Sun, Cong Xu, Hairong Lin, Zekun Deng
Abstract
The brain emotional learning network offers several advantages when compared to traditional neural networks. It features a simpler structure, low computational complexity, and fast training speed. These characteristics make it ideal for applications like pattern recognition, data classification, and intelligent control. However, current brain emotional learning networks, including their modified networks, are not capable of recognizing or classifying data in complex environments. To address this issue, this paper proposes a brain emotional learning network with an attention mechanism that strengthens the processing of key information while suppressing interfering information, thereby enabling the network to recognize data within complex environments. Furthermore, software implementation of neural networks often experiences slow computing speeds due to the separation of storage and computation in traditional von Neumann computers. To combat this issue, the paper presents a hardware circuit implementation of the attention mechanism-based brain emotional learning network using memristors. Finally, the designed in-memory computing neural network has been successfully applied to the recognition of traffic signs within complex environments, and has achieved accurate and rapid recognition.