arXiv 2610.181362026-10-13cs.AR

基于FPGA的纳米晶MEMS压力传感器神经网络补偿器的设计与实现

FPGA Based Design And Implementation of Neural Network Compensators for Nanocrystalline MEMS Pressure Sensor

Bengal Engineering & Science University（孟加拉工程技术大学）

Somsubhra Talapatra, Sudip Ghosh, C Roy Chaudhuri, Biplab K Sikdar, Hafizur Rahaman

备注：: 6 pages
发表信息：: VSI, TEQIP, DIT sponsored national conference on Design Techniques for modern electronic devices, VLSI and Communication Systems (DTVC- 2007) at NIT Hamirpur ,Himachal Pradesh, India

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AI总结：

该研究设计并在FPGA上实现神经网络补偿器，补偿纳米晶MEMS压力传感器的非线性与温度误差，构建的智能传感器系统可用于高速精确的生物医学压力测量。

摘要：

本文采用神经网络算法对纳米晶MEMS压力传感器的非线性和温度效应进行补偿，该算法已在FPGA芯片上实现。该神经网络采用反向传播算法训练多层感知器前馈网络，可将温度相关误差降低至98%。其在FPGA上采用流水线架构实现，以支持并行快速传感操作。该压力传感器与基于FPGA的补偿装置共同构成智能传感器系统，可实现高速、精确的压力测量，已针对典型生物医学应用进行测试。

英文摘要：

In this paper the effects of nonlinearity and temperature of nanocrystalline MEMS pressure sensor have been compensated using neural network algorithm which has been implemented on a FPGA chip. The neural network employs back propagation algorithm to train the multilayer perceptron feed forward network that causes an error reduction due to temperature upto the level of 98%. It is realized on FPGA with pipelined architecture for parallel and fast sensing operation. The pressure sensor along with the FPGA based compensation results in an intelligent sensor system capable of high speed and accurate pressure measurement which has been tested for a typical biomedical application.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。