一种非线性系统集员辨识算法

Set membership identification algorithm of nonlinear systems

针对带有未知有界噪声的非线性动态系统的鲁棒辨识问题,提出了一种新的非线性动态系统的集员辨识算法.利用径向基函数神经网络的逼近能力,根据系统的输入输出数据,选用径向基函数神经网络对未知非线性系统建模.径向基函数神经网络的中心被确定之后,考虑到建模误差与系统噪声有界,利用径向基函数神经网络为参数线性模型的特点,使用参数线性集员辨识算法辨识径向基函数神经网络的输出权值.由于集员辨识算法所得到的是网络输出权值的集合估计,在系统运行过程中,可以很方便地利用所建模型预测实际系统的输出范围.仿真表明,集员辨识算法辨识网络的输出权值比最小二乘法较少的受未知动态系统噪声分布的影响.

A new set membership identification algorithm was proposed for the robust identification problem of nonlinear dynamic systems with unknown but bounded noises. Radial basis function （RBF） networks were used to approximate unknown nonlinear dynamic systems utilizing their approximation ability according to input and output data of systems. The weights of the RBF network of the unknown nonlinear dynamic system were estimated using a linear-in-parameter set membership identification algorithm considering that the RBF network was a linear-in-parameter model and the modeling errors and system noises were bounded after the centers of the RBF network were determined. Since the result of the estimation was a set of the weights of the RBF network, it could be easily used to predict the interval of the practical system output. Simulation shows that the set membership algorithm is less affected by the distribution of the noises of the unknown nonlinear dynamic system than the least squares algorithm.