کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6958342 | 1451942 | 2016 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A composite discretization scheme for symbolic identification of complex systems
ترجمه فارسی عنوان
یک طرح اختیاری کامپوزیت برای شناسایی نمادین سیستم های پیچیده
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کلمات کلیدی
گسسته سازی، شناسایی نمادین،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
پردازش سیگنال
چکیده انگلیسی
Phase-space discretization is a necessary step for study of continuous dynamical systems using a symbolic dynamics and language-theoretic approach. It is also critical for many machine learning techniques, e.g., probabilistic graphical models (Bayesian Networks, Markov models). This paper proposes a novel composite discretization method - a univariate discretization, namely Statistical Similarity-based Discretization (SSD) followed by a multi-variate discretization called Maximally Bijective Discretization (MBD). While SSD first quantizes input variables for a complex system identifying different operating conditions, MBD finds a discretization on the output variables given the discretization on the input variables such that the correspondence between input and output variables in the continuous domain is preserved in discrete domain for the underlying dynamical system. The proposed method is applied on both simulated and experimental data and results are compared with classical uniform width, maximum entropy, clustering and self-organizing map based discretization techniques.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Signal Processing - Volume 125, August 2016, Pages 156-170
Journal: Signal Processing - Volume 125, August 2016, Pages 156-170
نویسندگان
Soumik Sarkar, Abhishek Srivastav,