Design And Research Of SAW Gas Sensor Temperature Compensation System Based On PSO-FNN

In recent years,environmental pollution has become increasingly serious,and the emission of harmful gases has also increased.Therefore,the application of sensors to gas detection is becoming more apparent.The surface acoustic wave gas sensor has the advantages of high sensitivity,high accuracy,low power consumption and easy integration,and can realize wireless passivity,and is particularly suitable for working in harsh environments such as high temperature and high pressure confined space.The surface acoustic wave(SAW)gas sensor is a new type of sensor.In actual work,it is still unavoidable to be disturbed by the external environment.Among them,temperature is the most important factor affecting its performance.This thesis aims to find a highly efficient SAW gas sensor temperature compensation method.Synthesizing the advanced technology in China and abroad for vibrating wire sensor,a fuzzy neural network based on particle swarm algorithm is proposed for temperature compensation.Comparing with the traditional BP neural network and fuzzy neural network compensation method,this algorithm based on particle swarm optimization Fuzzy neural network with higher accuracy and convergence speed.Using the characteristics of global optimization of the particle swarm algorithm,the performance of the fuzzy neural network is optimized,and the temperature drift of the output of the SAW gas sensor is reduced or even cancelled to a certain extent.The thesis makes a deep analysis and research on related theory and technology,including the structure and working principle of SAW gas sensors,particle swarm algorithm and fuzzy neural network;and a fuzzy neural network based on particle swarm algorithm to construct a temperature compensation model for SAW gas sensors,and selected the sample of the relevant data for simulation experiments.The experimental results are compared with the compensation results of fuzzy neural network.Finally,the hardware and software parts of the entire temperature compensation system are designed so that the compensation system based on PSO-Fuzzy Neural Network can be implemented by the DSP.