Modelling the variation in human decision making

Ozen, T.; Garibaldi, Jonathan M.; Musikasuwan, S.

doi:10.1109/nafips.2004.1337372

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…Middle fuzzy sets may be generated by the product of two such sigmoids where is the centre of the increasing sigmoid, is the centre of the decreasing sigmoid, and is the steepness of the slopes, or "width," of the sigmoid (which can in general be different for the increasing and decreasing sigmoid). For symmetrical middle sigmoids, as an implementation nicety, an adjustment can be made such that only two parameters are required, for example (3) where is the centre and is the width of the sigmoid. Fig.…”

Section: B Type-1 Fuzzy Systemsmentioning

confidence: 99%

“…For example, the case labeled the "worst" by the FES (rank 1) was also labeled "worst" by both experts at both repeats, so all four points are coincidental at location (1,1). For the case assigned rank 3 by the FES, expert A assigned ranks of 5 and 9, hence a triangle symbol appears at (3,5) and (3,9), while expert B assigned a rank of 6 in both repeats, hence the coincidental circle symbol at (3,6).…”

Section: E Determination Of Intraexpert Variabilitymentioning

confidence: 99%

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Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making

Garibaldi

Ozen

2007

IEEE Trans. Fuzzy Syst.

147

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Abstract-This paper presents a case study in which the introduction of vagueness or uncertainty into the membership functions of a fuzzy system was investigated in order to model the variation exhibited by experts in a medical decision-making context. A conventional (type-1) fuzzy expert system had previously been developed to assess the health of infants immediately after birth by analysis of the biochemical status of blood taken from infants' umbilical cords. Variation in decision making was introduced into the fuzzy expert system by means of membership functions which altered in small, predetermined manners over time. Three types of variation in membership functions were investigated: i) variation in the centre points, ii) variation in the widths, and iii) the addition of "white noise." Different levels (amounts) of uniformly distributed random variation were investigated for each of these types. Monte Carlo simulations were carried out to propagate the variation through the inferencing process in order to determine distributions of the conclusions reached. Interval valued type-2 fuzzy systems were also implemented to investigate the boundaries of variability in decisions. The results obtained were compared to the experts' decisions in order to determine which type and size of membership function variability best matched the experts' variability. The novel reasoning technique introduced in this study is termed nonstationary fuzzy reasoning.Index Terms-Interval type-2 fuzzy expert systems, medical decision making, nonstationary fuzzy reasoning, nonstationary type-1 fuzzy expert systems, umbilical acid-base assessment.

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Section: B Type-1 Fuzzy Systemsmentioning

confidence: 99%

Section: E Determination Of Intraexpert Variabilitymentioning

confidence: 99%

Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making

Garibaldi

Ozen

2007

IEEE Trans. Fuzzy Syst.

147

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“…Probability theory handles this question. When the two sorts of uncertainties happen simultaneously in a system, fuzzy random sets, fuzzy random variables, fuzzy probability and non-deterministic fuzzy set are usually used to characterise them [2] [77]. Fuzzy randomness simultaneously describes objective and subjective information as a fuzzy set of possible probabilistic models over some range of imprecision.…”

Section: Formalisation Of Uncertaintymentioning

confidence: 99%

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

John

Zhou²,

Garibaldi

et al. 2008

IJCIR

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Abstract:In the real world, group decision making is one of the most significant and omnipresent human decision making activities. The central problem of group decision making is to develop "fair" methods for aggregating individual alternatives (options, variants, etc.) to yield a consensus decision that is most acceptable to the group as a whole. It has become a subject of intensive research due to its practical and academic significance. But group decision making is innately uncertain, so a promising framework of decision making in groups is to consider uncertainty measures in the processes of model construction, so as to achieve optimal solutions in terms of best degrees of consensus of final decisions. This paper aims to provide an in-depth overview of group decision making incorporating uncertainty from the perspective of fuzzy sets (including type-1 and type-2 fuzzy sets). Some potential new research directions on fuzzy group decision making are suggested.

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“…The IT2FLC is particularly suitable for time-variant systems with unknown time-varying dynamics [22] . Though fuzzy control is the most widely used application of fuzzy set theory, T2FLC have been used in very few control applications, such as nonlinear control and mobile robot navigation [22] , decision making [23] , and quality control of sound speakers [24] . In order to obtain optimal performance of control systems, optimization algorithms, such as genetic algorithm (GA) and particle swarm optimization (PSO), have been frequently used [25] .…”

Section: Introductionmentioning

confidence: 99%

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

Zirkohi

Fateh

Shoorehdeli

2013

Int. J. Autom. Comput.

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Type-1 fuzzy sets cannot fully handle the uncertainties. To overcome the problem, type-2 fuzzy sets have been proposed. The novelty of this paper is using interval type-2 fuzzy logic controller (IT2FLC) to control a flexible-joint robot with voltage control strategy. In order to take into account the whole robotic system including the dynamics of actuators and the robot manipulator, the voltages of motors are used as inputs of the system. To highlight the capabilities of the control system, a flexible joint robot which is highly nonlinear, heavily coupled and uncertain is used. In addition, to improve the control performance, the parameters of the primary membership functions of IT2FLC are optimized using particle swarm optimization (PSO). A comparative study between the proposed IT2FLC and type-1 fuzzy logic controller (T1FLC) is presented to better assess their respective performance in presence of external disturbance and unmodelled dynamics. Stability analysis is presented and the effectiveness of the proposed control approach is demonstrated by simulations using a two-link flexible-joint robot driven by permanent magnet direct current motors. Simulation results show the superiority of the IT2FLC over the T1FLC in terms of accuracy, robustness and interpretability.

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Modelling the variation in human decision making

Cited by 24 publications

References 15 publications

Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making

Uncertain Fuzzy Reasoning: A Case Study in Modelling Expert Decision Making

Automated Group Decision Support Systems Under Uncertainty: Trends and Future Research

Type-2 Fuzzy Control for a Flexible- joint Robot Using Voltage Control Strategy

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