基于ICPSO-BP神经网络的光纤SPR传感器开环系统优化研究

Optimization of open-loop system of optical fiber SPR sensor based on ICPSO-BP neural network

表面等离子共振(SPR)传感器开环系统的弊端,对全局搜索粒子群算法(PSO)的早熟收敛问题进行改进,提出了一种动态信息调整且速度可控的改进型合作粒子群算法(ICPSO)。该方法通过在粒子飞行状态控制的迭代方程中引入子群最优信息,较好地保持了粒子多样性,有效地避免寻优飞行中粒子的早熟收敛。进一步将该算法作为BP神经网络的训练算法,建立了更为优化的ICPSO-BP神经网络。最后,利用ICPSO-BP神经网络对光纤SPR开环系统的内部非线性模型进行辨识补偿,分别建立单输入、双输入、三输入的ICPSO-BP神经网络补偿模型,实验及仿真结果表明新算法在测试线性精度和速度上均具有较好的表现,从而保证了光纤SPR良好的线性测试效果,为光纤SPR传感器进一步应用打下一定基础。

Aiming at the disadvantages of optical fiber SPR open-loop system,the premature convergence problem of particle swarm optimization(PSO)with global search is improved,and an improved cooperative particle swarm optimization(ICPSO)algorithm with dynamic information adjustment and controllable speed is proposed.By introducing the optimal information of subgroups into the iterative equation of the flight-state controlling of particle,the particles′diversity is well maintained,and the premature convergence of particles in optimization flight can be effectively avoided for the proposed algorithm.Furthermore,the algorithm is used as the training algorithm of BP neural network,and a more optimized ICPSO-BP neural network is established.Finally,the ICPSO-BP neural network is used to identify and compensate the internal nonlinear model of optical fiber SPR open-loop system,and the compensation models of single-input,double-input and three-input ICPSO-BP neural network are established respectively.The simulation results show that the new algorithm has good performance in speed and accuracy test,thus ensuring the good linearity test effect of SPR,and laying a foundation for the further application of optical fiber SPR sensor.