MS Intelligent Transportation Systems

The overall contents of this thesis are organized around two main parts. The first part is concerned with the modeling of optimization problems while the second part deals with the analysis of optimization problems. As introduction to the issues related to both modeling and analysis of optimization scenarios, we provide an explanatory state-of-the-art over-viewing the concepts of Lagrange Relaxation and Neuron Dynamics to explore possible answers to the various research questions concerning to different optimization issues. As proof of concept, we envisage a second order ordinary differential equation and we introduce a novel approach based on Cellular Neural Network (CNN) templates optimization to derive appropriate solutions. To achieve this, we combine the concepts of Lagrange Relaxation and Neuron Dynamics. 

Keywords: Optimization, neuron dynamics, cellular neural networks


Neuron Dynamics based CNN's template optimization for solving ODEs


Relevant Publications:
  1. U.A.Khan, “A Theoretical Framework for Optimization Issues With Applications: Survey, Analysis and Classification of Existing Optimization Approaches With the Theoretical Framework for Optimization Issues”, VDM Verlag, ISBN: 978-3-639-33499-9, 2010. [View]
  2. J.C. Chedjou, K. Kyamakya, M.A.Latif, U.A.Khan, “Solving Stiff Ordinary Differential Equations and Partial Differential Equations Using Analog Computing Based on Cellular Neural Networks”, In ISAST Transactions on Computers and Intelligent Systems, Vol.1, No. 2, pp. 38 - 46., 2009. [Download]