A. Bonarini and G. Bontempi. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation, 4(4):258--313, 1994.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of subspace models that are known to be inconsistent with the measurements. Semi quantitative system identification performs refinement at the qualitative and interval level. Semi quantitative system identification has also been applied to model based monitoring [11] Bonarini and Bontempi [4] have developed a quite similar approach to our consistency check. However, they have focused on uncertainty initial state values, which are given as intervals. Also related to our work is Armengol et al. 1, 2] The simulation is based on modal interval arithmetics, which produces overbounded and ....

Andrea Bonarini and Gianluca Bontempi, `A Qualitative Simulation Approach for Fuzzy Dynamic Models', ACM Transactions on Modeling and Computer Simulation, 4(4), 285--313, (1994).

....rules to describe the interesting aspects of the observed phenomenon. Ideally the modeler should should be able to cope with uncertainty and approximation if he wants to use the model to understand and to forecast the behavior of a complex, real system. Recently work has been done in this area. [6] designed and implemented the Qualitative Simulators. These simulators allow the description and simulation of approximate models, integrating fuzzy and traditional mathematics. The simulators implement the following features: ffl they accept ODE (ordinary differential equations) based and ....

A. Bonarini and G. Bontempi. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4(4):258--313, 1994.

....all fuzzy parameters in the initial data, and then apply the extension principle to the equation and to the time evaluation map at time t. This gives a fuzzy solution concept of (23) for which t S t ( is the unique solution (see [22] Our approach is equivalent to the one using the ow in [2] and in [23] We remark that other nonequivalent approaches have been undertaken: imbedding fuzzy sets into metric spaces [5, 13, 14, 25] di erentiation of bounding curves of level sets [13, 15, 27] parametrized fuzzy numbers [24] see also [4, 6, 13] for a study of the interrelations. For ....

A. Bonarini and G. Bontempi. A qualitative simulation approach for fuzzy dynamical models. ACM Trans. Modeling Comp. Sim., 4:285-313, 1994.

....inconsistency Deduction by fuzzy simulation and consistency checking (by quantitative comparison and interactive user control) Figure 3: A knowledge acquisition cycle using fuzzy simulation simulation. For such cases, we can use either qualitative or quantitative simulation with fuzzy set concepts [1, 18, 8] for the deduction process. However, a well known problem in using the qualitative methods is the possibly generating of spurious behaviors of the system during the reasoning process [12, 11] Moreover, in order to get a compressed and generalized set of fuzzy rules, additional methods such as ....

....as an attempt to deal with a type of imprecision which arises when the boundaries of classes are not sharply defined. A fuzzy set A of a universe of discourse X is characterized by a membership function A : X [0; 1] which associates with each element x of X a number A (x) in the interval [0, 1] which represents the grade of membership of x in A. Definition 2.1: A fuzzy set A of the universe of discourse X is convex if and only if for all x 1 ; x 2 in X, A (x 1 (1 Gamma )x 2 ) Min( A (x 1 ) A (x 2 ) where 2 [0; 1] Definition 2.2: A fuzzy set A of the universe of discourse X ....

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A. Bonarini and G. Bontempi. A Qualitative Simulation Approach for Fuzzy Dynamical Models. acm Trans. on Modeling and Computer Simulation, 4(4):285--313, 1994.

....a qualitative simulator do not correspond to any of the possible behaviours of the underlying physical system. These spurious solutions make it difficult to apply qualitative simulation to large, complex systems. Reducing the amount of spurious solutions is an area of intense research. Recently, (Bonarini and Bontempi 1994) proposed a qualitative simulation approach, based on interval simulation, that does not generate spurious solutions. In this contribution we use an interval simulation method as well to systematically identify possible model candidates, numerical ranges of the unknown parameters and initial ....

.... The analytic basis to handle and solve such a fuzzy dynamical model is provided by Zadeh s extension principle (Zadeh 1975) and Nguyen s identity (Nguyen 1978) Several fuzzy qualitative simulation methods that generate solutions of fuzzy dynamical models have been proposed, e.g. Qua.Si I and II (Bonarini and Bontempi 1994), Qua.Si III (Bontempi 1996) FuSim (Shen and Leitch 1993) and FRenSi (Keller, Wyatt, and Leitch 1999) FRenSi, Qua.Si II, and Qua.Si III do a fuzzy simulation by splitting the fuzzy region, formed by the fuzzy values of system variables and model parameters, into ff cuts using Nguyen s identity, ....

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Bonarini, A. and Bontempi, G. (1994). A qualitative simulation approach for fuzzy dynamical models, ACM Transactions on Modeling and Computer Simulation 4(4): 285--313.

....stochastic di erential equations and the evolution of the possibility distribution in deterministic dynamical system with fuzzy initial conditions and or parameters. The experimental contribution concerns an extension of the fuzzy simulation technique Qua.Si. Qualitative Simulator) proposed by Bonarini and Bontempi (1994; 1996) This method simulates the evolution of possibility distributions in di erential continuous systems where initial conditions and or parameters are described by fuzzy numbers. The Qua.Si. technique is compared with the Monte Carlo method on two dynamical systems, where uncertainty is ....

....di erential equation whose initial conditions belong to 0h . 6.1.2 Numerical solution of IDE To solve numerically the system (28) it might seem intuitive to apply the interval mathematics (Moore, 1966; Neumaier, 1990) directly to the numerical algorithms introduced in Section 2.1. However, in (Bonarini Bontempi, 1994) we have shown that a straightforward use of interval mathematics for the resolution of fuzzy (or interval) di erential equation may yield incorrect results. This is due to the fact that the fuzzy (and interval) formalism is unable to represent the interaction that the di erential equation ....

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Bonarini A. & Bontempi G. 1994. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4(4).

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BONARINI A. AND BONTEMPI G. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4, 4, (1994).

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BONARINI A. AND BONTEMPI G. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation (TOMACS), 4, 4, (1994b).

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A. Bonarini and G. Bontempi. A qualitative simulation approach for fuzzy dynamical models. ACM Transactions on Modeling and Computer Simulation, 4(4):258--313, 1994.

No context found.

Andrea Bonarini and Gianluca Bontempi. A Qualitative Simulation Approach for Fuzzy Dynamic Models. ACM Transactions on Modeling and Computer Simulation, 4(4):285--313, 1994.

No context found.

A. Bonarini and G. Bontempi, "A Qualitative Simulation Approach for Fuzzy Dynamical Models," acm Trans. on Modeling and Computer Simulation 4(4), pp. 285--313, 1994.

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