基于神经状态空间的非线性系统建模研究

An Approach of Nonlinear System Modeling Based on Neural State Space Model

提出了一种基于神经状态空间的非线性系统建模方法。神经状态空间(NNSP)具有系统的拟线性特性,许多线性系统控制器设计方法均可以扩展到 NNSP模型。本文采用了增广卡尔曼滤波方法进行神经状态空间的参数辨识,高阶校验模型用于验证非线性系统神经状态空间的模型的有效性 。将本法应用于典型的化学过程的建模,结果表明本方法正确有效。

In this paper, an approach of nonlinear system modeling based on neural state space model is proposed. The neural state space model is of the quasi-linear characteristics of system. Many linear system controller design approach, therefore, can be extended to apply to the NNSP models. The EKF approach is adopted for parameter identification of neural state space models and a high-order correction method is then applied to test the validity of the neural state space model of nonlinear systems. The application of this method to dynamic modeling of typical chemical processes shows that the presented approach is effective.