Luc Jaulin PhD, Michel Kieffer PhD, Olivier Didrit PhD, Éric's Applied Interval Analysis: With Examples in Parameter and PDF

By Luc Jaulin PhD, Michel Kieffer PhD, Olivier Didrit PhD, Éric Walter PhD (auth.)

ISBN-10: 1447102495

ISBN-13: 9781447102496

ISBN-10: 1447110676

ISBN-13: 9781447110675

At the middle of many engineering difficulties is the answer of units of equa­ tions and inequalities, and the optimization of expense features. regrettably, other than in specified situations, equivalent to whilst a suite of equations is linear in its un­ knowns or while a convex price functionality should be minimized below convex constraints, the implications bought through traditional numerical equipment are just neighborhood and can't be assured. this implies, for instance, that the particular worldwide minimal of a price functionality is probably not reached, or that a few worldwide minimizers of this expense functionality could get away detection. against this, period research makes it attainable to procure assured approximations of the set of the entire genuine options of the matter being thought of. This, including the inability of books proposing period strategies in this sort of approach that they can develop into a part of any engineering numerical instrument package, encouraged the writing of this publication. the journey began in 1991 with the education by means of Luc Jaulin of his PhD thesis, below Eric Walter's supervision. It persevered with their joint supervision of Olivier Didrit's and Michel Kieffer's PhD theses. greater than years in the past, once we awarded our booklet venture to Springer, we naively proposal that redaction will be an easy topic, given what had already been completed . . .

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Additional info for Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics

Sample text

Assume that f is differentiable over the box [x], and denote mid([xl) by m. , a column vector with entries gi = af laxi, i = 1, ... ,n. 105) where [g T] is an inclusion function for g T, so f ([xl) C;; f (m) + [gT] ([xl) ([x] - m) . 107) is an inclusion function for f, which we shall call the centred inclusion function. 108) for any given [x]. This function can be viewed as affine in x with an uncertain slope belonging to [l] ([xl). 10 for decreasing widths of [x]. The smaller w([xl) is, the better the cone approximates the function.

7. Reuniting sibling subpavings; (i) sibling leaves, (ii) sibling subpavings Intersecting subpavings: if X E RSP ([xl) and Y E RSP ([xl) , then Z = X n Y is also a subpaving of RSP ([xl).

Can be similarly extended to intervals. For instance, if b ~ c, ([a, b] ~ [c, dl) ([a, b] ~ ([a, b] ~ [c, dl) = 0 if a> d, [c, dl) = [0,1] if neither b ~ c nor a > d. 153) The Boolean comparison operator = cannot be extended in this way, because it has already been given a bivalued meaning by set theory: ([a, bl = [c, dl) = 1 if a = c and b = d, 0 otherwise. 154) With the help of interval analysis and the notion of inclusion function, it is easy to build an inclusion test for any test that can be put in the form t (x) = fJ(h (x), ...

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Applied Interval Analysis: With Examples in Parameter and State Estimation, Robust Control and Robotics by Luc Jaulin PhD, Michel Kieffer PhD, Olivier Didrit PhD, Éric Walter PhD (auth.)


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