International Journal of Mechatronics, Electrical and Computer Technology Vol. 3(7), Apr, 2013, Special Number, pp 1036-1047, ISSN: 2305-0543 Available online at: http://www.aeuso.org © Austrian E-Journals of Universal Scientific Organization - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 1036 A New Survey of types of Uncertainties in Nonlinear System with Fuzzy Theory Fereshteh Mohammadi and Mohammad Bazmara * School of Electrical and Computer Engineering, Shiraz University, Iran *Corresponding Author E-mail: Mohamad.bazmara@gmail.com Abstract This paper is an attempt to introduce a new framework to handle both uncertainty and time in spatial domain. The application of the fuzzy temporal constraint network (FTCN) method is proposed for representation and reasoning of uncertain temporal data. A brief introduction of the fuzzy sets theory is followed by description of the FTCN method with its main algorithms. The paper then discusses the issues of incorporating fuzzy approach into current spatio-temporal processing framework. The general temporal data model is extended to accommodate uncertainties with temporal data and relationships among events. A theoretical FTCN process of fuzzy transition for the imprecise information is introduced with an example. A summary of the paper is given together with outlining some contributions of the paper and future research directions. Keywords: Fuzzy α-cut, transformation method, transport model, Uncertainty, Variability. 1. Introduction Uncertainty is intrinsically imbedded in system modeling and parameters being the main cause of uncertainty in output values. Mechanistic modeling of physical systems is often complicated by the presence of uncertainties. Commonly environmental models are calibrated to field data to demonstrate their ability to reproduce contaminant behavior at site. However, solute transport modeling presents a big uncertainty due to the lack of reliable field data. On the other hand, specific field situations cannot be extrapolated over