Sentence examples for many nonlinear systems from inspiring English sources

In the past two decades, the T-S fuzzy model has been used to represent many nonlinear systems.

In 1990s, chaos, a very universal phenomenon in many nonlinear systems, has also been found valuable in secure communication systems due to its extreme sensitivity to initial conditions and parameters [4].

Classically, it is known that many nonlinear systems exhibit very complex behaviors of potentially interesting features [3], and it is currently believed that such features might be potentially useful in many areas of investigation, even beyond physics.

Although this method is used in many nonlinear systems for its simplicity, the precision is limited in the systems with strong nonlinearity and the fussy Jacobian matrix should be calculated which will inevitably increase the computational load.

Although this work is motivated by the multi-rate state estimation of biological systems, the proposed scheme and the performed analysis have a larger applicability to many nonlinear systems.

The improved fuzzy PI controller not only can control (stable and unstable) conventional linear systems, performing as well as the conventional PI controller, but also is capable of controlling many nonlinear systems such as the flexible-joint robot arm under investigation which contains uncertainties within 10% tolerance of all nominal system-parameter values.

Many nonlinear dynamic systems whose state-trajectory solutions do not exhibit finite escape times also possess this property.

The answers have proved instrumental, over the years, in establishing robustness of various stability notions and have served as the starting point for many nonlinear control systems design concepts.

In many nonlinear signaling systems, the signaling elements can give rise to discrete steady states.

It is also well known that the applicability of the adaptive backstepping control method is limited by unmodeled dynamics existing in many practical nonlinear systems.

For many applications, nonlinear systems can be modeled in additive white Gaussian noise environments with the state and observation equations as follows: x n = f n ( x n − 1 ) + B n w n, (96).