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An Integrated Neural Network Structure for Recognizing Autocorrelated and Trending Processes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Karaoglan, Aslan Deniz |
| Copyright Year | 2011 |
| 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. Key WordsControl Chart Pattern Recognition, Neural Networks, Trend AR(1) |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://mcajournal.cbu.edu.tr/volume16/vol16no2/v16no2p514.pdf |
| Alternate Webpage(s) | http://www.mdpi.com/1300-686X/16/1/514/pdf |
| Alternate Webpage(s) | http://www.mdpi.com/2297-8747/16/2/514/pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Autocorrelation Autoregressive model Biological Neural Networks Learning vector quantization Nevus sebaceous Numerous Pattern recognition Perceptron Quad Flat No-leads package Recurrent neural network Stationary process |
| Content Type | Text |
| Resource Type | Article |
