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We use Abdollahian et al's parameter estimates from their genetic algorithm non-linear least squares (GANLS) estimation procedure on WVS data to populate system coefficients to increase model validity.
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We apply control at one boundary, take measurements on the other boundary, and allow the system's functional coefficients to be unknown.
To solve the identification problem of multivariable nonlinear discrete-time systems, each of estimated system coefficients represents a gene, and a collection of genes is referred to as a chromosome in the view of GA.
However, most of the results on the absolute stability of Lurie systems require that the system coefficients be bounded.
As depicted in Figure 2, system (5) is asymptotically stable even though the system coefficients are unbounded.
Taking the system coefficients as parameters, explicit expressions for the first 11 quasi-Lyapunov constants are deduced.
The conditions do not require analytical solutions but only bounds and an appropriate norm space assumption for uncertainties of the system coefficients matrix.
Based on these approximations, we present closed-form power allocation strategies which involve several key system coefficients and can be easily implemented.
The approach is also suitable for determining the propagation of fuzziness in automatic control and dynamical systems where all system coefficients are expressed as fuzzy parameters.
The states and system coefficients equations can then be summarized as: (4) The last two equations determine the current system coefficients, which will be appended to the system state.
This study is crucial to provide more accurate regression coefficients to system designers when designing link margins.
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