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Ivancevic_Applied-Diff-Geom

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<strong>Applied</strong> Manifold <strong>Geom</strong>etry 393completely known systems where qualitative values are expressed by intervals.However, qualitative simulation techniques reveal a low predictivepower in presence of complex models. In this section we have combinedqualitative and quantitative methods, in spirit of [Bontempi (1995);<strong>Ivancevic</strong> and Snoswell (2001)].In this section we will deal with the general biodynamics from the pointof view that mathematically and logically approaches a general theory ofsystems, i.e., that makes the unique framework for both linear and nonlinear,discrete and continuous, deterministic and stochastic, crisp and fuzzy,SISO and MIMO–systems, and generalizes the robot dynamics elaboratedin the literature (see [Vukobratovic (1970); Vukobratovic et al. (1970);Vukobratovic and Stepanenko (1972); Vukobratovic and Stepanenko (1973);Vukobratovic (1975); Igarashi and Nogai (1992); Hurmuzlu (1993); Shihet al. (1993); Shih and Klein (1993)]), including all necessary DOF tomatch the physiologically realistic human–like motion. Yet, we wish toavoid all the mentioned fundamental system obstacles. To achieve this goalwe have formulated the general biodynamics functor machine, covering aunion of the three intersected frameworks:(1) Muscle–driven, dissipative, Hamiltonian (nonlinear, both discrete andcontinuous) MIMO–system;(2) Stochastic forces (including dissipative fluctuations and ‘Master’jumps); and(3) Fuzzy system numbers.The Abstract Functor MachineIn this subsection we define the abstract functor machine [<strong>Ivancevic</strong> andSnoswell (2001)] (compare with [Anderson et al. (1976)]) by a two–stepgeneralization of the Kalman’s modular theory of linear MIMO–systems[Kalman et. al. (1969); Kalman (1960)]. The first generalization putsthe Kalman’s theory into the category Vect of vector spaces and linearoperators (see [MacLane (1971)] for technical details about categorical language),thus formulating the unique, categorical formalism valid both forthe discrete– and continuous–time MIMO–systems.We start with the unique, continual–sequential state equationẋ(t + 1) = Ax(t) + Bu(t), y(t) = Cx(t), (3.205)where the nD vector spaces of state X ∋ x, input U ∋ u, and outputY ∋ y have the corresponding linear operators, respectively A : X → X,

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