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Math. Comput. Appl. 2011, 16(2), 514-523; doi:10.3390/mca16020514

An Integrated Neural Network Structure for Recognizing Autocorrelated and Trending Processes

Department of Industrial Engineering, Dokuz Eylul University, 35160, Tinaztepe, Buca - Izmir, Turkey
Published: 1 August 2011
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Abstract

Data sets collected from industrial processes may have both a particular type of trend and correlation among adjacent observations (autocorrelation). In the present paper, an integrated neural network structure is used to recognize trend stationary first order autoregressive (trend AR(1)) process. The proposed integrated structure operates as follows. (i) First a combined neural network structure (CNN), that is composed of appropriate number of linear vector quantization (LVQ) and multi layer perceptron (MLP) neural networks, is used to recognize the trended data, (ii) then, the Elman’s recurrent neural network (ENN) is used to diagnose the autocorrelation through the data. Correct classification rate is used as performance criteria. Results indicate that proposed structure is effective and competitive with other combined neural network structures.
Keywords: Control Chart Pattern Recognition; Neural Networks; Trend AR(1) Control Chart Pattern Recognition; Neural Networks; Trend AR(1)
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Karaoglan, A.D. An Integrated Neural Network Structure for Recognizing Autocorrelated and Trending Processes. Math. Comput. Appl. 2011, 16, 514-523.

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