4842
12
3066
3
15595
document
3066
jpcmaf1.pdf
application/pdf
831493
20091208 20:55:15
http://cogprints.org/4842/1/jpcmaf1.pdf
4842
1
application/pdf
application/pdf
en
public
jpcmaf1.pdf

http://eprints.org/relation/hasVolatileVersion
http://cogprints.org/id/document/6430

http://eprints.org/relation/haspreviewThumbnailVersion
http://cogprints.org/id/document/6430

http://eprints.org/relation/hasVersion
http://cogprints.org/id/document/6430

http://eprints.org/relation/hasVolatileVersion
http://cogprints.org/id/document/8352

http://eprints.org/relation/hasVersion
http://cogprints.org/id/document/8352

http://eprints.org/relation/hasIndexCodesVersion
http://cogprints.org/id/document/8352
archive
6346
disk0/00/00/48/42
20060421
20110311 08:56:23
20070912 17:02:57
journalp
show
0
Many realworld processes tend to be chaotic and also do not lead to satisfactory analytical modelling. It has been shown here that for such chaotic processes represented through short chaotic noisy timeseries, a multiinput and multioutput recurrent neural networks model can be built which is capable of capturing the process trends and predicting the future values from any given starting condition. It is further shown that this capability can be achieved by the Recurrent Neural Network model when it is trained to very low value of mean squared error. Such a model can then be used for constructing the Bifurcation Diagram of the process leading to determination of desirable operating conditions. Further, this multiinput and multioutput model makes the process accessible for control using openloop/closedloop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of HÃ©non Map as a representative of some realworld processes.
 http://dx.doi.org/10.1016/j.jprocont.2005.04.002

Jallu
Krishnaiah
j.krishnaiah@gmail.com

C
Kumar
S

Faruqi
Aslam
M
pub
Bifurcation Diagram, Recurrent Neural Networks, Multivariate chaotic timeseries; Chaotic process
1
5366
FALSE
Journal of Process Control
TRUE
 compscimachdynamsys
 compscimachlearn
 compscicomplextheory
 compsciartintel
Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1â€”simulation studies
16
published
200601
public