将改进的粒子群优化（PSO）算法和函数联接型神经网络(FLANN)相结合，实现传感器的动态线性建模。利用传感器的动态标定实验数据，首先训练FLANN神经网络，网络训练结束后的权值作为粒子群中某个粒子的初始值，而后利用改进的PSO算法继续寻优，得到的全局最优值即为所求的传感器动态模型的系数。实验结果表明，该方法结合了PSO和FLANN两者的优点，建模精度高。

In this paper, the dynamic linear model of sensor is founded based on the improved particle swarm optimization (PSO) algorithm and function link artificial neural network (FLANN). According to measurement data in dynamic calibration ,the weights of network is used to initialize one particle station of whole particel swarm when the training of FLANN has finished.Then the improved PSO algorithm is applied ,the global best particle station of particle swarm is the coefficient of sensor’s dynamic model what we need. The experiment results show that this approach has excellence of both PSO and FLANN, meanwhile it has better precision.