The cost-reference particle filter (CRPF) is a variant of the particle filter (PF). It is simpler, more robust, and more flexible than the standard PF. Particularly, it does not require any statistical information on both state noises and observation noises in its application. However, in order to successfully apply CRPF to the nonlinear state estimation in dynamic process systems, the knowledge of the model parameters should be known a priori. The standard CRPF cannot handle the problem of state and parameter estimation (SPE) with unknown noise statistics and model parameters. To eliminate the above limitation, this paper proposes an evolution algorithm for the SPE in nonlinear dynamic process systems. The algorithm is the combination of the artificial evolution (AE) with CRPF, called AE-CRPF, to estimate the states and the parameters simultaneously. The proposed AE-CRPF is applied to two nonlinear dynamic process systems for practical applications. The results demonstrate the effectiveness and robustness of the proposed method.

成本参考粒子过滤器（CRPF）是粒子过滤器（PF）的一种变体。它比标准PF更简单，更强大，更灵活。特别地，在其应用中，它既不需要状态噪声也不需要观察噪声的任何统计信息。然而，为了成功地将CRPF应用于动态过程系统中的非线性状态估计，应该事先知道模型参数的知识。标准CRPF无法处理未知统计信息和模型参数的状态和参数估计（SPE）问题。为了消除上述限制，本文提出了一种非线性动态过程系统中SPE的演化算法。该算法是人工进化（AE）与CRPF（称为AE-CRPF）的组合，同时估计状态和参数。所提出的AE-CRPF应用于实际应用中的两个非线性动态过程系统。结果证明了该方法的有效性和鲁棒性。