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The internal rate of return (IRR) is among the most widely used characteristics of investment projects. In the deterministic case the IRR can be found as a solution to an algebraic equation. The existence and applicability of the IRR may be guaranteed by rather general conditions, e.g. those of Norstrom theorem. Under uncertainty, the IRR ceases to be uniquely determined even for typical projects (a project is typical if costs precede returns). Under uncertainty, the IRR can take values from a more or less large set of possible values. A key problem is to transform the information about initial parameters into a distribution of the IRR values. Probabilistic methods can be used if quite a number of stringent requirements are met. If an unusual or extraordinary project is considered the probabilistic approach is not sufficiently substantiated. In such cases possibilistic methods and fuzzy set theory seem to be more suitable. The present paper aims to provide a method for evaluating the IRR of investment projects with fuzzy cash flows using the possibility theory. Given a fuzzy cash flow, the IRR is presented as a fuzzy set and the membership function may be considered as a version of distribution. Under the standard addition of fuzzy numbers we give explicit formulas for the membership function of the IRR. If components of a fuzzy cash flow are correlated we use the addition of fuzzy numbers with respect to t-norms. Generally, a possibilistic evaluation of the IRR with respect to a non-standard t-norm is rather difficult and was not considered before. If the t-norm is generated by a convex additive generator we reduce the evaluation of the IRR to a common convex optimization problem. A numerical example is presented. We believe the proposed method can be applied to evaluating the efficiency of investment projects with fuzzy cash flows.

In this paper, a postulation on the relationship between the memory structure of the brain’s neural network and the representation of information granules in the semantic web is presented. In order to show this connection, abstract operations of inducing information granules are proposed to be used for the proposed logical operations systems, hereinafter referred to as: analysis, reduction, deduction and synthesis. Firstly, the searched information is compared with the information represented by the thesaurus, which is equivalent to the auto-diagnosis of this system. Secondly, triangular norm systems (information perception systems) are built for fuzzy or vague information. These are fuzzy sets. The introduced logical operations and their logical values, denoted as problematic, hypothetical, validity and decidability, are interpreted in these fuzzy sets. In this way, the granularity of the information retrieval on the Web is determined according to the type of reasoning.

Multibiomitric systems are expected to be more accurate due to the presence of multiple evidences, score level fusion is the most commonly used approach in multibiometrics. In this paper, A novel approach is proposed for the fusion at score level fusion based on False Reject Rate(FRR) and False Accept Rate(FRR) using triangular norms(t-norms). This study aims at tapping the potential of t-norms for information fusion at first, at the second, it transfers scores into Transfer function based on corresponding FRRs and FARs, thus avoiding calculating posterior probability of a certain score. Experiment result shown that the proposed method renders very good performance as it is quite computationally and outperforms the traditional score level fusion schemes, the experimental result also confirms the effectiveness of the proposed method to improve the performance of multibiometric system.