NDLI: Introducing recursive learning algorithm for system identification of nonlinear time varying processes

Content Provider	IEEE Xplore Digital Library
Author	Mirmomeni, M. Lucas, C. Araabi, B.N.
Copyright Year	2009
Description	Author affiliation: Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Eng., University of Tehran, Iran (Lucas, C.; Araabi, B.N.) \|\| Young Researchers Club, Islamic Azad University, Tehran, Iran (Mirmomeni, M.)
Abstract	several methods have been introduced for identification of nonlinear processes via locally or partially linear models. Unfortunately, most of these methods have a training phase which should be done offline. There are phenomena that possess time varying behavior. Furthermore, the amount, distribution and/or quality of measurement data that is available before the model is put to operation may be insufficient to build a model that would meet the specification. One of the most popular learning methods in nonlinear system identification is Locally Linear Model Tree (LoLiMoT) algorithm as an incremental learning method which needs to be carried out by an offline data set. This paper introduces a recursive version of this algorithm called Recursive Locally Linear Model Tree algorithm (RLoLiMoT) for time varying and online applications. The proposed method also eliminates some of the LoLiMoT restrictions in tuning premise parameters of the Locally Linear Models (LLMs). Two case studies are considered to test the performance of the proposed method. The results depict the power of the proposed method in online system identification of nonlinear time varying systems.
Starting Page	736
Ending Page	741
File Size	490234
Page Count	6
File Format	PDF
ISBN	9781424446841
DOI	10.1109/MED.2009.5164631
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-24
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	recursive learning RLoLiMoT Humans time varying Nonlinear control systems Power system modeling Learning systems nonlinear systems Time varying systems neurofuzzy Automatic control System identification Mathematical model system identification Nonlinear systems Testing
Content Type	Text
Resource Type	Article

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