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• W2045854568 abstract "One representation of the nonlinear dynamical system is the expansion by the functional series. Especially, the functional series expansion by Wiener has the feature that the kernel can be estimated relatively easily by inputting normal white noise. Consequently, it is used widely in the analysis of the neural system and other problems. However, few studies have been made on the quantitative evaluation of the accuracy of the kernel estimation because of its difficulty. This paper proposes a practical method of evaluating the accuracy of kernel estimation, thereby simplifying the problem by introducing appropriate restrictions and approximations into the kernel estimation. By this approach, the accuracy of kernel estimation can be represented as a function of the Volterra kernel of the system, the input power level, and the data length. The result is verified by a numerical experiment. It is shown that there exists the optimal input power level in the identification of the nonlinear system that maximizes the accuracy of kernel estimation. This is a new finding which has not been observed in the theory of the linear system." @default.
• W2045854568 created "2016-06-24" @default.
• W2045854568 creator A5047185748 @default.
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• W2045854568 date "1993-01-01" @default.
• W2045854568 modified "2023-10-02" @default.
• W2045854568 title "Precision analysis of wiener kernels measured by cross-correlation method" @default.
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• W2045854568 doi "https://doi.org/10.1002/ecjc.4430760302" @default.
• W2045854568 hasPublicationYear "1993" @default.
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