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Qualitative-fuzzy system identification of complex dynamical systems
2007 IEEE International Fuzzy Systems Conference, 2007Fuzzy systems have been proved to be excellent candidates for system dynamics identification. However, they are affected by two drawbacks: the resulting nonlinear model (i) does not guarantee that the generalization property holds unless a large amount of samples is employed, and (ii) is not understandable from a physical viewpoint. These drawbacks are
Guglielmann R, Ironi L
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Fuzzy Dynamic Programming with Stochastic Systems
1988Multistage decision making in a fuzzy environment (fuzzy constraints, fuzzy goals and fuzzy decisions) is considered. As a tool for solving these problems, fuzzy dynamic programming for the case of a deterministic and fuzzy system under control is provided. Then, the case of a stochastic system under control is discussed in detail. Two formulations are
Fedrizzi, Mario, A. Esogbue, J. Kacprzyk
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1994
The aim of this paper is to study fuzzy dynamic systems. The role of fuzzy sets in formation of a conceptual and computational platform for symbolic and numerical information processing is identified. We summarize essential properties of fuzzy partitions developed with the aid of families of fuzzy sets.
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The aim of this paper is to study fuzzy dynamic systems. The role of fuzzy sets in formation of a conceptual and computational platform for symbolic and numerical information processing is identified. We summarize essential properties of fuzzy partitions developed with the aid of families of fuzzy sets.
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Generators of fuzzy dynamical systems
Fuzzy Sets and Systems, 2000This work is devoted to the ergodic properties of fuzzy dynamical systems. To this end the authors introduce the generators of fuzzy dynamical system and present a fuzzy version of the Kolmogorov-Sinai theorem concerning the entropy of a fuzzy dynamical system. This theorem allows them to compute the entropy for a class of fuzzy systems. Moreover, they
Dumitrescu, D. +2 more
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Fuzzy modelling of dynamic systems
Proceedings of 26th Southeastern Symposium on System Theory, 2002This paper deals with fuzzy modeling of complex dynamic systems. Fuzzy relational equations are used to describe dynamic systems' behavior. A prediction algorithm is proposed and the procedure to update the fuzzy relation of the model as new measurements are collected is discussed. The algorithm is applied to the prediction of chaotic time-series. >
M.B. Ghalia, A.T. Alouani
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A continuous-time dynamic fuzzy system. (II). Fuzzy potentials
Fuzzy Sets and Systems, 2000This paper is devoted to fuzzy-number-valued functions on convex fuzzy sets. The author discusses fuzzy potentials in the continuous-time dynamic fuzzy system given in [Fuzzy Sets Syst. 113, 453-460 (2000; Zbl 0963.37010)]. Moreover, he gives a fuzzy relational differential equation to characterize the fuzzy potentials.
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Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002
Residual swing will often be generated in the overhead traveling crane drive by factors such as the delay and the friction of the machine even if it is controlled along the pattern. Therefore we have developed "KF dynamic fuzzy crane system" for automatic operation and put it to practical use.
O. Itoh, H. Migita, J. Itoh, Y. Irie
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Residual swing will often be generated in the overhead traveling crane drive by factors such as the delay and the friction of the machine even if it is controlled along the pattern. Therefore we have developed "KF dynamic fuzzy crane system" for automatic operation and put it to practical use.
O. Itoh, H. Migita, J. Itoh, Y. Irie
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Fuzzy Dynamic Programming Problem for Extremal Fuzzy Dynamic System
2010This work deals with the problems of the Expremal Fuzzy Continuous Dynamic System (EFCDS) optimization problems and briefly discuss the results developed by G. Sirbiladze [31]–[38]. The basic properties of extended extremal fuzzy measure are considered and several variants of their representation are given.
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Fuzzy System Dynamics of Manpower Systems
2014Manpower recruitment and training in uncertain and turbulent environments is a challenge to decision makers in large organizations. In the absence of numerical precision on market growth and the ensuing manpower demand, designing manpower planning policies is vital. Often times, companies incur losses due to overstaffing and/or understaffing.
Michael Mutingi, Charles Mbohwa
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Systems dynamics with fuzzy logic
International Journal of Systems Science, 1990Systems dynamics has been used to model and simulate a variety of environments, e.g. economic, social and political, which require quantification or some types of human behaviour. The lack of empirical verification of the relationships in the systems dynamics models has often been criticised.
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