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Verification, Validation, and Testing of Engineered Systems by Avner Engel

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6.4 FUZZY VVT COST MODELING

6.4.1 Introduction

A key problem associated with the VVT cost, time and risk estimation procedures presented in the previous sections is that input data such as costs, risk levels and VVT performance levels are inexact by nature. As a result, it is difficult to obtain valid data in order to populate the various VVT models. However, using a fuzzy logic paradigm can reduce this predicament greatly.

Specifically, parameters such as VVT costs can be better quantified in terms of minimum, most-likely and maximum values. Likewise, parameters such as levels of risk occurrence can be better encapsulated in linguistic terms such as “high” or “low” rather than in exact values of probability. This chapter extends the methodology for estimating the cost and risk of system VVT by modeling it by means of a fuzzy logic paradigm. The proposed fuzzy logic methodology for estimating cost and risk will be illustrated using a similar example of developing an avionics suite for a transport helicopter. This chapter demonstrates that applying the VVT cost and risk estimation methodology using a fuzzy logic paradigm yields beneficial results.

6.4.2 General Fuzzy Logic Modeling

Fuzzy theory was introduced by Lotfi Zadeh (1965) of the University of California at Berkeley in the 1960s. Fuzzy logic is a superset of conventional (Boolean) logic which has been extended to handle the concept of partial truth—truth values which are somewhere between “completely true” and “completely ...

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