Studies in Computational Intelligence
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Fuzzy without Fuzzy: Why Fuzzy-Related Aggregation Techniques Are Often Better Even in Situations without True Fuzziness
Studies in Computational Intelligence - - Trang 27-51 - 2009
Fuzzy techniques have been originally invented as a methodology that transforms the knowledge of experts formulated in terms of natural language into a precise computer-implementable form. There are many successful applications of this methodology to situations in...
Computing Statistics under Probabilistic and Interval Uncertainty: A Brief Description
Studies in Computational Intelligence - - Trang 9-10 - 2012
Computing statistics under probabilistic uncertainty. In the case of probabilistic uncertainty, we know the probability distributions for measurement errors corresponding to all the inputs x1,..., x
n
, and we want to find the...
From Computing Statistics under Interval and Fuzzy Uncertainty to Practical Applications: Need to Propagate the Statistics through Data Processing
Studies in Computational Intelligence - - Trang 251-259 - 2012
Need for data processing. In many areas of science and engineering, we are interested in a quantity y which is difficult (or even impossible) to measure directly. For example, it is difficult to directly measure the distance to a faraway star or the amount of oil in...
Applications to Information Management: How to Estimate Degree of Trust
Studies in Computational Intelligence - - Trang 277-281 - 2012
In this chapter, we use the probabilistic and interval uncertainty to estimate the degree of trust in an agent. Some of these results first appeared in [56, 141].
Results and Algorithms
In the traditional approach to trust, we either...
Beyond Interval Uncertainty: Taking Constraints into Account
Studies in Computational Intelligence - - Trang 335-347 - 2012
For set information, in addition to the interval bounds on each variables x1,..., x
n
, we may have additional information: e.g., we may know that the actual values should satisfy a constraint g(x1,..., x
n...
The Seasonal Affective Disorder Cycle on the Vietnam’s Stock Market
Studies in Computational Intelligence - - Trang 873-885 - 2019
In this study, the authors used the TGARCH(1,1) model according to three different distribution patterns: normal distribution (Gaussian distribution), Student-t distribution, and generalized error distribution (GED) to analyze the effect of Seasonal Affective...
Computing Higher Moments under Interval Uncertainty
Studies in Computational Intelligence - - Trang 153-166 - 2012
Higher central moments M
h
=
$\frac{1}{n}$
·...
Towards Selecting Appropriate Statistical Characteristics: The Basics of Decision Theory and the Notion of Utility
Studies in Computational Intelligence - - Trang 51-54 - 2012
In the previous chapter, we mentioned that in general, the problem of estimating statistical characteristics under interval uncertainty is NP-hard. This means, crudely speaking, that it is not possible to design a feasible algorithm that would compute all statistics...
How Reliable Is the Input Data?
Studies in Computational Intelligence - - Trang 237-242 - 2012
In traditional interval computations, we assume that the interval data corresponds to guaranteed interval bounds, and that fuzzy estimates provided by experts are correct. In practice, measuring instruments are not 100% reliable, and experts are not 100% reliable, we...
Generation of Test Data Using Genetic Algorithm and Constraint Solver
Studies in Computational Intelligence - - Trang 499-513 - 2017
Search-based testing techniques using genetic algorithm (GA) can automatically generate test data that achieves high coverage on almost any given program under test. GA casts the path coverage test data generation as an optimization problem and applies efficient...
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