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| Abstract: | Modeling of chaotic systems, based on the output time series, is quite promising since the output often represents the characteristic behaviour of the total system. It has been an interesting topic for researchers over the past few years. So far, some methods are developed for the identification of chaotic systems. Because of the intense complexity of chaotic systems, the performance of existing algorithms is not always satisfactory. Application of chaotic system theory to socially relevant problems like environmental studies is the need of the hour Neural networks have the required self-learning capability to tune the network parameters (i.e. weights) for identifying highly non-linear and chaotic systems. In the present work, effectiveness of modeling a chaotic system using dynamic neural networks has been demonstrated. From the rich literature available for non-linear modeling with neural networks, the Recurrent Neural Network (RNN) structure is selected. The Extended Kalman Filter (EKF) algorithm is used to train the RNN. Further, the Expectation Maximization algorithm is used to effectively arrive at the initial states and the state covariance. Particle filter algorithm with its two important variants namely Sampling Importance Resampling (SIR) and Rao Blackwellised algorithms are also used for training the given RNN. Four standard chaotic systems, Lorenz, Rossler, Chua and Chen, are modelled with the three algorithms. The best algorithm is found to be EKF-EM based on the least mean square error criterion. Validation of RNN model with EKFEM algorithm is done in time domain by Estimation of embedding dimension, Phase plots, Lyapunov Exponents, Kaplan -Yorke dimension and Bifurcation diagrams. Analysis of the chaotic systems is also performed in the transform domain using Fourier, Wavelet and Mapped Real Transforms. viii Natural chaotic systems are analyzed based on the selected model structure and training algorithm, taken for analysis. Sunspot, Venice Lagoon and North Atlantic oscillations are the three of the natural chaotic systems modelled with the selected RNN model structure and EKF-EM algorithm. |
| URI: | http://dyuthi.cusat.ac.in/purl/5162 |
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| Dyuthi-T2196.pdf | (6.188Mb) |
| Abstract: | This thesis deals with the use of simulation as a problem-solving tool to solve a few logistic system related problems. More specifically it relates to studies on transport terminals. Transport terminals are key elements in the supply chains of industrial systems. One of the problems related to use of simulation is that of the multiplicity of models needed to study different problems. There is a need for development of methodologies related to conceptual modelling which will help reduce the number of models needed. Three different logistic terminal systems Viz. a railway yard, container terminal of apart and airport terminal were selected as cases for this study. The standard methodology for simulation development consisting of system study and data collection, conceptual model design, detailed model design and development, model verification and validation, experimentation, and analysis of results, reporting of finding were carried out. We found that models could be classified into tightly pre-scheduled, moderately pre-scheduled and unscheduled systems. Three types simulation models( called TYPE 1, TYPE 2 and TYPE 3) of various terminal operations were developed in the simulation package Extend. All models were of the type discrete-event simulation. Simulation models were successfully used to help solve strategic, tactical and operational problems related to three important logistic terminals as set in our objectives. From the point of contribution to conceptual modelling we have demonstrated that clubbing problems into operational, tactical and strategic and matching them with tightly pre-scheduled, moderately pre-scheduled and unscheduled systems is a good workable approach which reduces the number of models needed to study different terminal related problems. |
| Description: | School of Engineering,Cochin University of Science and Technology |
| URI: | http://dyuthi.cusat.ac.in/purl/2818 |
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| Dyuthi-T0838.pdf | (11.23Mb) |
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