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This serves as a starting point to cast our system identification problem into a Bayesian framework.
The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem.
In order to test the system for functionality and implementation results, a widely used standard system identification problem is implemented.
Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges.
This paper aims to study the new methodology of nonlinear system identification problem of one stage spur gearbox.
We then apply this sampler to a system identification problem for a non-linear hysteretic structure model to investigate this method under globally identifiable and unidentifiable conditions.
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We present efficient, high-probability learning algorithms for a number of system identification problems involving such errors.
In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems.
The use of a signal distribution theoretic framework facilitates the formulation and analysis of many system modelling problems, including system identification problems.
Experimental results prove the effectiveness of the proposed combined architectures in exploiting any sparseness degree and improving the modeling performance in nonlinear system identification problems.
Regarding the empirical evaluation, simulation results involving nonlinear dynamic system identification problems are reported, contrasting the performance shown by the MSVME approach with that exhibited by conventional SVM and ME models.
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