کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
494928 862810 2015 17 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
State estimation of nonlinear dynamic systems using weighted variance-based adaptive particle swarm optimization
ترجمه فارسی عنوان
برآورد دولت از سیستم های پویا غیر خطی با استفاده از بهینه سازی ذرات تطبیقی ​​مبتنی بر واریانس وزنی
کلمات کلیدی
فیلتر هوشیاری، اندازه جمعیت سازگار، بهینه سازی ذرات ذرات، تکامل دیفرانسیل
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی


• A new heuristic filter of adaptive particle swarm optimization (APSO) is developed.
• The swarm size is adaptively tuned based on the weighted variance of the particles.
• The proposed APSO is compared with a variety of heuristic optimization algorithms.
• Performance of the proposed filter is compared with variety of existing filters.
• APSO is applicable for state estimation of every nonlinear/non-Gaussian system.

New heuristic filters are proposed for state estimation of nonlinear dynamic systems based on particle swarm optimization (PSO) and differential evolution (DE). The methodology converts state estimation problem into dynamic optimization to find the best estimate recursively. In the proposed strategy the particle number is adaptively set based on the weighted variance of the particles. To have a filter with minimal parameter settings, PSO with exponential distribution (PSO-E) is selected in conjunction with jDE to self-adapt the other control parameters. The performance of the proposed adaptive evolutionary algorithms i.e. adaptive PSO-E, adaptive DE and adaptive jDE is studied through a comparative study on a suite of well-known uni- and multi-modal benchmark functions. The results indicate an improved performance of the adaptive algorithms relative to original simple versions. Further, the performance of the proposed heuristic filters generally called adaptive particle swarm filters (APSF) or adaptive differential evolution filters (ADEF) are evaluated using different linear (nonlinear)/Gaussian (non-Gaussian) test systems. Comparison of the results to those of the extended Kalman filter, unscented Kalman filter, and particle filter indicate that the adopted strategy fulfills the essential requirements of accuracy for nonlinear state estimation.

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ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 34, September 2015, Pages 1–17
نویسندگان
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