Author/Authors
James C. Wong، نويسنده , , Karen A. McDonald and Ahmet Palazoglu، نويسنده ,
DocumentNumber
1384271
Title Of Article
Classification of process trends based on fuzzified symbolic representation and hidden Markov models
شماره ركورد
11128
Latin Abstract
This paper presents a strategy to represent and classify process data for detection of abnormal operating
conditions. In representing the data, a wavelet-based smoothing algorithm is used to filter the high frequency
noise. A shape analysis technique called triangular episodes then converts the smoothed data into a
semi-qualitative form. Two membership functions are implemented to transform the quantitative information
in the triangular episodes to a purely symbolic representation. The symbolic data is classified with
a set of sequence matching hidden Markov models (HMMs), and the classification is improved by utilizing
a time correlated HMM after the sequence matching HMM. The method is tested on simulations with a
non-isothermal CSTR and compared with methods that use a back-propagation neural network with and
without an ARX model.
From Page
395
NaturalLanguageKeyword
Trend detection , Hidden Markov models
JournalTitle
Studia Iranica
To Page
408
To Page
408
Link To Document