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Stochastic distribution control (SDC) for non-Gaussian system is a mathematically complicated yet practical problem to solve. The most recent solution involves a radial basis function neural network (RBFNN) framework to approximate non-Gaussian output probability density function (PDF). The dynamic weights of such neural network are controlled within each batch of ILC, using a dedicated adaptive controller...
The paper presents an approach to system identification of input/output mappings of non-linear stochastic systems in accordance to an information-theoretic criterion. At that, a parameterized description of the system under study is utilized combined with a corresponding technique of estimation of the mutual information (in the Shannon sense), leading, finally, to a problem of the finite dimensional...
Stochastic distribution control systems aims at the controller design so as to realize a shape control of the distributions of certain random variables in the process. Once the probability density functions (PDFs) of these variables are used to describe their distributions, the control task is to obtain control signals so that the output PFDs of the system are made to follow their target PDFs. In...
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