New Development in Nonlinear System Modeling

由于自然、社会和工业系统存在非线性，非线性系统建模在分析、设计、控制和优化这些系统中无处不在。在此基础上，我们基于局部建模和进化计算的思想发展了非线性系统建模的新方法。调查的主题是：1。局部建模中运行状态的自动选择由于全局模型的性能很大程度上受到局部模型识别的局部运行状态的选择的影响，因此开发了合适的局部状态的自动选择算法。2 .面向块的非线性模型识别采用进化计算方法，对非线性静态部分和线性动态部分级联的面向块的非线性模型进行识别。基于径向基函数网络的非线性建模从多目标优化问题出发，讨论了非线性建模中径向基函数网络的辨识问题。基于进化策略的粒子滤波非线性滤波将蒙特卡罗方法中基于重要性滤波的粒子滤波与进化计算中的进化策略相结合，提出了一种基于进化策略的非线性滤波方法。

Since there exist nonlinearities in natal, social and industrial systems, nonlinear system modeling is ubiquitous for analysis, design, control, and optimization of such systems. In this we have developed new methodologies for nonlinear system modeling based on the idea of local modeling and evolutionary computation. The topics investigated are :1. Automatic operating regime selection in local modelingSince the performance of the global model is highly affected by the choice of the local operating regimes in which the local models are identified, automatic selection algorithms of suitable local regimes have been developed.2. Identification of block oriented nonlinear modelsUsing evolutionary computation approaches, identification method for block oriented nonlinear models, in which nonlinear static part and linear dynamic part are in cascade connection.3. Nonlinear modeling by Radial basis function networkIdentification of radial basis function network for nonlinear modeling has been discussed from the multi-objective optimization problem.4. Evolution strategies based particle filter for nonlinear filteringA novel nonlinear filter called evolution strategies based nonlinear filter has been developed combining particle filter based on the importance filtering in Monte Carlo method and evolution strategies in evolutionary computation.