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In this paper, the problem of identifying nonlinear systems under adaptive binary-valued output measurements is considered. We follow a nonparametric approach, which directly estimates the value of the nonlinear function representing the system at any fixed point with the help of a recursive kernel-based stochastic approximation algorithm with expanding truncations (SAAWET). The thresholds of the...
In this paper, the nonparametric identification of nonlinear systems with binary-valued output observations is considered. The kernel-based stochastic approximation algorithm with expanding truncations (SAAWET) is proposed to recursively estimate the value of a nonlinear function representing the system at any fixed point. All estimates are proved to converge to the true values with probability one...
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