基于UKF 和神经网络的一类非线性系统状态估计

在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.

State estimation of a class of nonlinear system based on UKF and neural network

It is significant to estimate the important process variables when process models are unknown. Therefore, the method of combining unscented Kalman filter(UKF) with neural network is used to solve the state estimation problems for a class of nonlinear systems whose models are unknown. The dynamic neural network is used to model for the nonlinear system, and the state and weights are updated at the same time by using UKF, which can achieve the purposes that the neural network approximate the real model, and the estimated values follow the real values. Two simulation examples are given to verify that the proposed approach gets good effects of estimation, and the greater the proportion of state in the output, the higher the estimation precision.