title: Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Networks models Part 2 - Industrial Study
creator: Jallu, Krishnaiah
creator: Kumar, C.S.
creator: Faruqi, M.A.
subject: Dynamical Systems
subject: Machine Learning
subject: Neural Nets
description: Many real-world processes tend to be chaotic and are not amenable to satisfactory analytical models. It has been shown here that for such chaotic processes represented through short chaotic noisy observed data, a multi-input and multi-output recurrent neural network can be built which is capable of capturing the process trends and predicting the behaviour for 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 multi-input and multi-output model makes the process accessible for control using open-loop / closed-loop approaches or bifurcation control etc.
publisher: Elsevier
date: 2006-01
type: Journal (Paginated)
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/4881/1/jpc-maf2.pdf
identifier:   Jallu, Krishnaiah and Kumar, C.S. and Faruqi, M.A.  (2006) Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Networks models Part 2 - Industrial Study.  [Journal (Paginated)]     
relation: http://cogprints.org/4881/