Design of Multi-MCU Communication Circuit Based on Deep Learning
This article aims to explore how deep learning technology can be utilized to optimize multi-master communication systems in microcontrollers,thereby enhancing their reliability and efficiency in industrial environments.Using the STM32F407 as the core controller,we designed hardware circuits for multi-master communication based on the RS-485 bus,and proposed an adaptive communication protocol combining Long Short-Term Memory(LSTM)and Convolutional Neural Networks(CNN)along with an intelligent decision-making mechanism based on Deep Reinforcement Learning(DRL).Experimental results show that under different levels of electromagnetic interference,compared with traditional protocols,the proposed scheme significantly improves in terms of communication success rate,transmission delay,throughput,error rate,and energy efficiency,with more pronounced advantages in highly interfering environments.The research indicates that deep-learning-based communication schemes offer new solutions for industrial communication systems.
deep learningmicrocontroller multi-node communicationSTM32F407
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