Approximation and its implementation process of the stochastic hybrid fuzzy system

Approximation and its implementation process of the stochastic hybrid fuzzy system The stochastic Mamdani and Takagi–Sugeno fuzzy systems are firstly unified in a random environment, and the resulting stochastic hybrid fuzzy system is established according to some stochastic parameters. Secondly, A canonical representation of the stochastic process with orthogonal increments is presented by the properties of the Lebesgue–Stieltjes measure and stochastic integral, the approximation of the stochastic hybrid fuzzy system in the mean square sense is proved. Finally, an implementation process of this system is described through a simulation example, and the surface figure of the covariance function shows that the stochastic hybrid fuzzy system has excellent approximation capability. International Journal of Machine Learning and Cybernetics Springer Journals

Approximation and its implementation process of the stochastic hybrid fuzzy system

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Springer Berlin Heidelberg
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
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