82,076 research outputs found
Modelling and control of chaotic processes through their Bifurcation Diagrams generated with the help of Recurrent Neural Network models: Part 1—simulation studies
Many real-world 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 time-series, a multi-input and multi-output 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 multi-input and multi-output model makes the process accessible for control using open-loop/closed-loop approaches or bifurcation control etc. All these studies have been carried out using a low dimensional discrete chaotic system of Hénon Map as a representative of some real-world processes
Modelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational Intelligence
The main objective of the present work is to develop a framework for modelling and controlling of a real world multi-input and multi-output (MIMO) continuously drifting metallurgical process, which is shown to be a complex system. A small change in the properties of the charge composition may lead to entirely different outcome of the process. The newly emerging paradigm of soft-computing or Hybrid Computational Intelligence Systems approach which is based on neural networks, fuzzy sets, genetic algorithms and chaos theory has been applied to tackle this problem In this framework first a feed-forward neuro-model has been developed based on the data collected from a working Submerged Arc Furnace (SAF). Then the process is analysed for the existence of the chaos with the chaos theory (calculating indices like embedding dimension, Lyapunov exponent etc). After that an effort is made to evolve a fuzzy logic controller for the dynamical process using combination of genetic algorithms and the neural networks based forward model to predict the system’s behaviour or conditions in advance and to further suggest modifications to be made to achieve the desired results
Ferromagnetism and the Effect of Free Charge Carriers on Electric Polarization in Y_2NiMnO_6 Double Perovskite
The double perovskite Y_2NiMnO_6 displays ferromagnetic transition at Tc = 81
K. The ferromagnetic order at low temperature is confirmed by the saturation
value of magnetization (M_s) and also, validated by the refined ordered
magnetic moment values extracted from neutron powder diffraction data at 10 K.
This way, the dominant Mn4+ and Ni2+ cationic ordering is confirmed. The
cation-ordered P 21/n nuclear structure is revealed by neutron powder
diffraction studies at 300 and 10 K. Analysis of frequency dependent dielectric
constant and equivalent circuit analysis of impedance data takes into account
the bulk contribution to total dielectric constant. This reveals an anomaly
which coincides with the ferromagnetic transition temperature (T_c).
Pyrocurrent measurements register a current flow with onset near Tc and a peak
at 57 K that shifts with temperature ramp rate. The extrinsic nature of the
observed pyrocurrent is established by employing a special protocol
measurement. It is realized that the origin is due to re-orientation of
electric dipoles created by the free charge carriers and not by spontaneous
electric polarization at variance with recently reported magnetism-driven
ferroelectricity in this materialComment: Published in Physical Review
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