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Article title IDENTIFICATION AND ESTIMATION OF STATES FOR FUZZY DYNAMICAL SYSTEMS
Authors N.P. Voronova, S.M. Kovalev A.N. Shabelnikov
Section SECTION III. SIMULATION AND DESIGN
Month, Year 06, 2016 @en
Index UDC 519.71
DOI
Abstract Conventional approaches to the control of complex dynamical objects in uncertainties are based on analytical models presented in form of differential and difference equations. However, the construction of analytical models is impossible for semi-formalized objects, when various non-factors are occurred. Because of this fact, intelligent models based on human expert knowledges are most preferable ones. Among these models, fuzzy dynamical systems play an important role. The paper presents a decision of general tasks connected with identification, prediction and estimation of states for fuzzy dynamical systems describing the behavior of semi-formalized dynamical objects. A new approach to statement estimation and parameter identification for fuzzy dynamical systems is considered. The basis of the considered approach is adaptive network model of the computation of fuzzy prior and posterior estimates of system’s state variables taking place in consequent time steps. Optimization of the model parameters is taking into account in the approach as well. Proposed technique for parameter identification has the set of fundamentally new properties. Among them, possibility of integration into a system of empirical expert knowledges, higher level of potential accuracy of identification based on possibility of utilizing the generalized fuzzy criteria of optimality and real-time identification of model parameters (because of small number of iterations required for optimal states estimation) are highlighted. An example of optimal parameter estimation for fuzzy dynamical system is considered and experimental results are presented. Experimental verification shows that estimates of identified parameters found based on developed simplex algorithm are not deviated from real values more than by 10% in many cases for wide variety of fuzzy dynamical systems.

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Keywords Fuzzy dynamical system; conditional membership function; prior fuzzy distribution; posterior fuzzy distribution; adaptive network model; parametric identification
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