Introduction

Knacks of evolutionary computing paradigm-based heuristics has been exploited exhaustively for system modeling and parameter estimation of complex nonlinear systems due to their legacy of reliable convergence, accurate performance, simple conceptual design ease implementation ease and wider applicability.

Objectives

The aim of the presented study is to investigate in evolutionary heuristics of weighted differential evolution (WDE) to estimate the parameters of Hammerstein-Wiener model (HWM) along with comparative evaluation from state-of-the-art counterparts. The objective function of the HWM for controlled autoregressive systems is efficaciously formulated by approximating error in mean square sense by computing difference between true and estimated parameters.

Methods

The adjustable parameters of HWM are estimated through heuristics of WDE and genetic algorithms (GAs) for different degrees of freedom and noise levels for exhaustive, comprehensive, and robust analysis on multiple autonomous trials.

Results

Comparison through sufficient large number of graphical and numerical illustrations of outcomes for single and multiple execution of WDE and GAs through different performance measuring metrics of precision, convergence and complexity proves the worth and value of the designed WDE algorithm. Statistical assessment studies further prove the efficacy of the proposed scheme.

Conclusion

Extensive simulation based experimentations on measure of central tendency and variance authenticate the effectiveness of the designed methodology WDE as precise, efficient, stable, and robust computing platform for system identification of HWM for controlled autoregressive scenarios.

中文翻译：

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用于 Hammerstein-Wiener 系统参数估计的加权差分进化的新设计

介绍

基于进化计算范式的启发式方法的诀窍已被详尽地用于复杂非线性系统的系统建模和参数估计，因为它们具有可靠的收敛性、准确的性能、简单的概念设计、易于实施和更广泛的适用性。

目标

本研究的目的是研究加权差分进化 (WDE) 的进化启发式，以估计 Hammerstein-Wiener 模型 (HWM) 的参数以及来自最先进同行的比较评估。受控自回归系统的 HWM 目标函数是通过计算真实参数和估计参数之间的差异来近似均方意义上的误差来有效地制定的。

方法

HWM 的可调参数是通过 WDE 的启发式算法和遗传算法 (GA) 针对不同的自由度和噪声水平进行估计的，以便对多个自主试验进行详尽、全面和稳健的分析。

结果

通过对 WDE 和 GA 的单次和多次执行的结果进行足够大量的图形和数字说明比较，通过精度、收敛性和复杂性的不同性能测量指标证明了所设计的 WDE 算法的价值和价值。统计评估研究进一步证明了所提出方案的有效性。

结论

基于集中趋势和方差测量的广泛模拟实验验证了设计方法 WDE 的有效性，作为精确、高效、稳定和强大的计算平台，用于受控自回归场景的 HWM 系统识别。