Handling Uncertainty by Mathematics by Hans Bandemer

By Hans Bandemer

"Mathematics of Uncertainty" offers the fundamental principles and foundations of uncertainty, masking the fields of arithmetic within which uncertainty, variability, imprecision and fuzziness of information are of value. This introductory publication describes the elemental principles of the mathematical fields of uncertainty from basic interpolation to wavelets, from errors propagation to fuzzy units and neural networks. The e-book provides the therapy of difficulties of interpolation and approximation, in addition to statement fuzziness that may primarily impact the preciseness and reliability of statements on practical relationships. The notions of randomness and chance are tested as a version for the variety of remark and size effects. along with those uncomplicated principles the publication additionally offers tools of qualitative info research corresponding to cluster research and type, and of evaluate of practical relationships resembling regression research and quantitative fuzzy info research.

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10) i=1 ˆm ) can be given in a closed form (which Although the solution (ˆ a0 , . . , a will be presented and considered in Subsect. 2) at this point it is assumed for the moment that the solution is given by a computer output. 11) j=0 With that the considerations are mostly finished and the unknown function is identified with the obtained polynomial approximation. This thoughtless and dangerous inference, however, needs some critical assessment: By the given procedure the deviations between the functional values and the approximating values become known only at the observation points, the data.

By methods of interval mathematics (see Subsect. 2). On the second way one will put a polynomial of higher order over each interval and will remove the ambiguity of the coefficients by demanding smoothness of the curve, when crossing from an interval to a neighbouring one. 3 Local Approximation 21 Again the three intervals are considered: ∆1 = [x1 , x2 ]; ∆2 = [x2 , x3 ]; ∆3 = [x3 , x4 ]. Over each interval a cubical parabola is defined: ∆1 : y = a01 + a11 x + a21 x2 + a31 x3 ∆2 : y = a02 + a12 x + a22 x2 + a32 x3 ∆3 : y = a03 + a13 x + a23 x2 + a33 x3 .

F (t + q) = f (t), then by the scale transformation x = (2π/q)t and with the 26 2 Mathematical Representation of Simple Data and Connections system of functions 1, cos x, sin x, cos(2x), sin(2x), . . 17) j=1 as it is called. Other representations are possible and usual. Likewise there are corresponding setups for the multidimensional case. For the numerical determination of the coefficients a0 , aj , bj ; j = 1, . . , m/2 appropriate software exists, at least for the usually equidistant observation points xi .

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