TY  - GEN
ID  - cogprints4881
UR  - http://cogprints.org/4881/
A1  - Jallu, Krishnaiah
A1  - Kumar, C.S.
A1  - Faruqi, M.A.
Y1  - 2006/01//
N2  - 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.
PB  - Elsevier
KW  - Bifurcation Diagram
KW  -  Recurrent Neural Networks
KW  -  Multivariate
Chaotic Time-series
KW  -  Chaotic Process
TI  - Modelling and control of chaotic processes
through their Bifurcation Diagrams generated
with the help of Recurrent Neural Networks
models Part 2 - Industrial Study
SP  - 67
AV  - public
EP  - 79
ER  -

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