Operators’ response time estimation for a critical task using the fuzzy logic theory

2008 ◽  
pp. 319-328
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.


Author(s):  
N. Samarinas ◽  
C. Evangelides

Abstract The aim of this paper is to implement the fuzzy logic theory in order to estimate the discharge for open channels, which is a well-known physical problem affected by many factors. The problem can be solved by Manning equation but the parameters present uncertainties as to their true-real values. Especially, the Manning n roughness coefficient, which is an empirically derived coefficient, presents quite high variation for different substrates. With the help of fuzzy logic and utilizing a fuzzy transformation method, it is possible to include the uncertainties of the problem in the calculation process. In this case, it is feasible to estimate the discharge, giving more emphasis on different uncertainty rates of the Manning roughness coefficient, while the rest of the parameters remain with constant or zero uncertainty level. By taking different a-cut levels, it was shown that the methodology gives realistic and reliable results, presenting with great accuracy the variations of the water discharge for trapezoidal open channels. This way, a possible underestimation or overestimation of the actual physical condition is avoided, by helping the engineers and researchers to obtain a more comprehensive view of the real physical conditions, thus making better management plans.


Endeavour ◽  
1996 ◽  
Vol 20 (1) ◽  
pp. 44 ◽  
Author(s):  
Dennis H. Rouvray

1987 ◽  
Vol 2 (2) ◽  
pp. 75-97 ◽  
Author(s):  
Alessandro Saffiotti

AbstractThis paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.


Sign in / Sign up

Export Citation Format

Share Document