In the process of investment decision making, next to financial indicators many other aspects of investment projects are increasingly often considered. This leads to the multicriteria evaluation of a project. The last decades have shown that the number and complexity of dependencies both inside and outside a company makes it difficult to use the probability theory to represent all kinds of the ...

The ability of an agent to make quick, rational decisions in an uncertain environment is paramount for its applicability in realistic settings. Markov Decision Processes (MDP) provide such a framework, but can only model uncertainty that can be expressed as probabilities. Possibilistic counterparts of MDPs allow to model imprecise beliefs, yet they cannot accurately represent probabilistic sour...

in this paper an efficient three- stage algorithm is developed for software production cost and time estimation. first stage includes a hybrid model composed of cocomo and function points methods to increase estimation accuracy. second stage encompasses paired comparisons matrix of analytical hierarchy process to determine amount of any resources consumed in each step of software production by ...

This paper presents an approach to apply L1 adaptive control for output regulation in the presence of nonlinear uncertainty in underactuated hybrid systems with application to bipedal walking. The reference model is generated by control Lyapunov function based quadratic program (CLFQP) controller and is nonlinear. We evaluate our proposed control design on a model of RABBIT, a five-link planar ...

We consider a model for the risk-based design of a flood protection dike, and use probability distributions to represent aleatory uncertainty and possibility distributions to describe the epistemic uncertainty associated to the poorly known parameters of such probability distributions. A hybrid method is introduced to hierarchically propagate the two types of uncertainty, and the results are co...

This paper reviews non-probabilistic approaches of modelling uncertainty, particularly in flood forecasting and introduces a fuzzy set theory-based method for treating precipitation uncertainty in rainfall-runoff modelling, which allows the temporal and/or spatial disaggregation of precipitation. The results of the fuzzy set theory-based method are compared with the probabilistic approach using...

Both neutrosophic sets theory and rough sets theory are emerging as powerful tool for managing uncertainty, indeterminate, incomplete and imprecise information .In this paper we develop an hybrid structure called “ rough neutrosophic sets” and studied their properties.