BP Neural Network based on PSO Algorithm for Temperature Characteristics of Gas Nanosensor

To comprehensively understand the characteristics of gas nanosensor between temperature and sensitivity, this paper has developed a Backward Propagation (BP) neural network based on Particle Swarm Optimization (PSO), which is applied to fitting the temperature-sensitivity characteristic of the SnO2 gas nanosensor mixed with benzene. The simulation results show the PSO can well optimize the structure of the BP network, and the fitting accuracy of the temperature of nanosensor using the acquired BP model is improved greatly and the optimized BP network has better generalization performance than the traditional BP network, and the acquired curve is both smooth and accurate, so the study shows that BP-PSO neural network is effective for fitting the temperature characteristics of gas nanosensor.