The uncertain nonlinear systems can bemodeled with fuzzy equations by incorporating the fuzzy set theory. In this paper, the fuzzy equations are applied as the models for the uncertain nonlinear systems. The nonlinear modeling process is to find the coefficients of the fuzzy equations.We use the neural networks to approximate the coefficients of the fuzzy equations.The approximation theory for ...

An important aspect of mathematical modeling for controller design or system performance evaluation is obtaining an appropriate model order that meets the needs of the problem at hand. In modeling systems with distributed and discrete mechanical components, a model builder needs to decide the number of compliant elements to use to represent each distributed component — a potentially timeconsumi...

A nonlinear parameterizable macromodel fo r CMOS Operational Transconductance Amplijiers (OTAs) is presented. The macromodel captures the DC, AC and large signal features of the transistor level original circuit, thus modeling accurately its time domain and nonlinear performance. The main contribution of this work is to account for the bias-dependent transconductance nonlinearities in the circu...

Neural network black-box modeling is usually performed using nonlinear input-output models. The goal of this paper is to show that there are advantages in using nonlinear state-space models, which constitute a larger class of nonlinear dynamical models, and their corresponding state-space neural predictors. We recall the fundamentals of both input-output and state-space black-box modeling, and ...

A great many of processes in a nature are nonlinear, so their modeling requires an embedding of nonlinear parts into the model structure. One of the popular approaches to the nonlinear system modeling are Volterra series. Unfortunately, already the second order Volterra kernel requires high amount of coefficients for its identification and therefore a large number of computations for its realiz...

The robust control problem for nonlinear systems is discussed from the standpoint of the amplitude sensitivity of the nonlinear plant and nal control system. Failure to recognize and accommodate this factor may give rise to nonlinear control systems that behave diierently for small versus large input excitation, or perhaps exhibit limit cycles or instability. Sinusoidal-input describing functio...

The robust control problem for nonlinear systems is discussed from the standpoint of the amplitude sensitivity of the nonlinear plant and final control system. Failure to recognize and accommodate this factor may give rise to nonlinear control systems that behave differently for small versus large input excitation, or perhaps exhibit limit cycles or instability. Sinusoidal-input describing func...

This paper presents the modeling of highly nonlinearcircuits using a total-variation-decreasing (TVD) differencescheme developed for the simulation of problems involving shockphenomena. In contrast to the commonly used leapfrog scheme,a second-order accurate TVD method based on the Lax–Wen-droff scheme is applied to one-dimensional nonlinear transientelectromagnetic-wave...

comparison of linear and nonlinear (bilinear) time series models in reference crop evapotranspiration prediction in urmia synoptic stationreference crop evapotranspiration (eto) prediction is one of the important elements in optimizing agricultural water consumption. in this regard, one of the prediction approaches is to use the stochastic time series methods. in this research, ar (p) and arma(...

This work deals with the fault detection and localization in the case of uncertain nonlinear systems. The presented method uses the diagnosis based on mathematical models. To model nonlinear systems, the multiple model approach is used. This method uses the Takagi-Sugeno fuzzy systems principle to obtain a nonlinear system named multiple models. This modeling principle has the advantage of obta...