Titlebook: Decision Making under Constraints; Martine Ceberio,Vladik Kreinovich Book 2020 Springer Nature Switzerland AG 2020 Computational Intellige

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陰郁

Probabilistic Fuzzy Neural Networks and Interval Arithmetic Techniques for Forecasting Equities,al network (PFNN) (developed by Han-Xiong Li and Zhi Liu), we are able to handle complex stochastic uncertainties. In light of the Black Scholes Merton model, we use triangular fuzzy numbers in its calculaltion to include the range instead of a spot price for an investment. Stock market forecasting

古代

Prostatism

Thick Separators,pped with the inclusion . as an order relation. It can generally be defined by parameters or functions which are not known exactly, but are known to belong to some intervals. In this paper, we show how to use constraint propagation methods in order to compute efficiently an inner and an outer approx

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AMPLE

Attraction-Repulsion Forces Between Biological Cells: A Theoretical Explanation of Empirical Formul attract. There are empirical formulas that describe the dependence of the corresponding forces on the distance between the cells. In this paper, we provide a theoretical explanation for these empirical formulas.

眨眼

When We Know the Number of Local Maxima, Then We Can Compute All of Them, the locations of all its local maxima. We show, however, that if we know the . of local maxima, then such an algorithm is already possible. Interestingly, for global maxima, the situation is different: even if we only know the number of locations where the . maximum is attained, then, in general, i

GEAR

Why Decimal System and Binary System Are the Most Widely Used: A Possible Explanation,t with a unit interval and we want to construct an interval of half width, then this width is exactly 5/10; when we want to find a square of half area, its sides are almost exactly 7/10, and when we want to construct a cube of half volume its sides are almost exactly 8/10. In this paper, we show tha

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SCORE

Ontology Matching with Knowledge Rulesmuch smaller quantum computation time .. In this paper, we show that for a continuous optimization problem, with quantum computing, we can reach almost the same speed-up: namely, we can reduce the non-quantum time . to a much shorter quantum computation time ..