As such, fuzzy sets constitute a powerful approach not only to deal with incomplete, noisy, or imprecise data but also to help in developing uncertain models of the data that provide smarter and smoother performance than traditional systems.
The uncertain problem is developed; considering uncertain parameters, the suggested dual-objective facility programming model is written.
In this paper, a new reliability analysis technique is developed for uncertain structures based on a hybrid uncertain model.
How the unfinished Edwin Drood (1870) would have developed is uncertain.
A robust recursive design technique is developed for uncertain nonlinear plants in vectorial strict feedback form.
Based on the result, a generalized H2 output feedback controller is developed for uncertain discrete-time switched systems.
In recent years, many approximation-based adaptive fuzzy or neural backstepping controllers have been developed for uncertain nonlinear systems; see [1 19].
Then to improve the transient performance of adaptive systems by tuning the design parameters, by further combining appropriately Lyapunov-Krasovskii functionals with adaptive backstepping technique, adaptive control method is developed for uncertain nonlinear strict-feedback systems with time delays [28 30].
In this paper, a robust intelligent tracking control (RITC) system employs an adaptive output recurrent cerebellar model articulation controller (ORCMAC) is developed for uncertain nonlinear system to achieve H∞ tracking performance.
Robust optimization techniques are developed for uncertain data described by several known distributions including a uniform distribution, a normal distribution, the difference of two normal distributions, a general discrete distribution, a binomial distribution, and a poisson distribution.
