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Modelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational Intelligence

Jallu, Krishnaiah and C, S Kumar and Roy, A K and Faruqi, M Aslam (2002) Modelling of Metallurgical Processes Using Chaos Theory and Hybrid Computational Intelligence. [Conference Paper]

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Abstract

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.

Item Type:Conference Paper
Keywords:Hybrid Computational Intelligence, Recurrent Neural Networks, Multivariate chaotic time-series; Chaotic process
Subjects:Computer Science > Dynamical Systems
Computer Science > Machine Learning
Computer Science > Complexity Theory
Computer Science > Artificial Intelligence
ID Code:4882
Deposited By:Dr., Jallu Krishnaiah
Deposited On:25 May 2006
Last Modified:11 Mar 2011 08:56

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