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Both the control parameters, i.e., α and δ, highly influence the overall performance of the NLMS algorithm.
This term contains both the control parameters, i.e., α and δ, and also the statistical information on the input signal.
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Moreover, robustness with respect to both fluctuations in the control parameters and dissipation can be achieved using complimentary advanced control techniques, such as a description in Liouville space and ensemble optimization [31, 43].
Both the disturbances and the control parameters have an influence on the stability of the dynamical system.
Both terms depend on the control parameters, α and δ, as well as on the filter length, L, and the input signal power, ({sigma _{x}^{2}}).
When we use the grey-fuzzy control scheme to design the constant turning force system, it is necessary to adjust the control parameters of both the grey predictor and the fuzzy controller (i.e., the sample size and grey constants of the grey predictor, and the scaling factors of the fuzzy controller) for guaranteeing stability and obtaining optimal control performances.
When the grey-fuzzy control scheme is used to design the constant turning force system, it is necessary to adjust the control parameters of both the grey predictor and the fuzzy controller (i.e., the sample size and grey constants of the grey predictor, and the scaling factors of the fuzzy controller) for ensuring stability and obtaining optimal control performances.
The control parameters for both PSO and GA approaches are depicted in Table 2.
After the survey was conducted, the control parameters for both methods are extracted and compared with each other in Table 5.
The laser power, frequency, pulse width and translation speed were the control parameters in both studies; hence a direct comparison was established between the model and the experimental results.
The control parameters to be tuned were the membership functions.
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