Computer Engineering Commons^™

A Broad Prospective On Fuzzy Transforms: From Gauging Accuracy Of Quantity Estimates To Gauging Accuracy And Resolution Of Measuring Physical Fields, Vladik Kreinovich, Irina Perfilieva

Departmental Technical Reports (CS)

Fuzzy transform is a new type of function transforms that has been successfully used in different application. In this paper, we provide a broad prospective on fuzzy transform. Specifically, we show that fuzzy transform naturally appears when, in addition to measurement uncertainty, we also encounter another type of localization uncertainty: that the measured value may come not only from the desired location x, but also from the nearby locations.

Model Fusion Under Probabilistic And Interval Uncertainty, With Application To Earth Sciences, Omar Ochoa, Aaron A. Velasco, Christian Servin, Vladik Kreinovich

Departmental Technical Reports (CS)

One of the most important studies of the earth sciences is that of the Earth's interior structure. There are many sources of data for Earth tomography models: first-arrival passive seismic data (from the actual earthquakes), first-arrival active seismic data (from the seismic experiments), gravity data, and surface waves. Currently, each of these datasets is processed separately, resulting in several different Earth models that have specific coverage areas, different spatial resolutions and varying degrees of accuracy. These models often provide complimentary geophysical information on earth structure (P and S wave velocity structure).

Combining the information derived from each requires a joint …

Quantum Computations Techniques For Gauging Reliability Of Interval And Fuzzy Data, Luc Longpre, Christian Servin, Vladik Kreinovich

Departmental Technical Reports (CS)

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 may have estimates which are "way off", intervals which do not contain the actual values at all. Usually, we know the percentage of such outlier un-reliable measurements. However, it is desirable to check that the reliability of the actual data is indeed within the given percentage. The problem of checking (gauging) this reliability is, in general, NP-hard; in reasonable cases, there …

Towards Neural-Based Understanding Of The Cauchy Deviate Method For Processing Interval And Fuzzy Uncertainty, Vladik Kreinovich, Hung T. Nguyen

Departmental Technical Reports (CS)

One of the most efficient techniques for processing interval and fuzzy data is a Monte-Carlo type technique of Cauchy deviates that uses Cauchy distributions. This technique is mathematically valid, but somewhat counterintuitive. In this paper, following the ideas of Paul Werbos, we provide a natural neural network explanation for this technique.