In this paper, inference model is proposed so as to analyze nonlinearly loaded dipole antenna. In modeling process, linear and nonlinear behavior of the problem is saved as simple and unchanged membership functions and the effect of incident wave on the induced voltage at different harmonies are then extracted easily. Consequently the model achieved is more efficient than previous studies using...

Detecting the watermark from the rescanned image is still a challenging problem, especially for high resolution image. After printing and scanning, an image usually suffers global geometric distorts ( such as rotation, scaling, translation and cropping), local random nonlinear geometric distortions which is simulated in Random Bending function of Stirmark, as well as nonlinear pixel value disto...

parameter estimation of the nonlinear muskingum model is a highly nonlinear optimization problem. although various techniques have been applied to optimize the coefficients of the nonlinear muskingum flood routing models, but an efficient method for this purpose in the calibration process is still lacking. the accuracy of artificial bee colony (abc) algorithm is investigated in this paper to op...

Virtually all chemical processes are nonlinear, but for several of them linear feedback control is adequate. Therefore, before nonlinear controller design is attempted, it is natural to ask “When is linear control adequate for a nonlinear process?” to ensure favorable cost/benefit ratio. Consequently, methods are needed that quantify the nonlinearity of a process within the context of assessing...

A special nonlinear fuzzy controller is presented to control the output signal of an undamped one-degree-of-freedom oscillating system with nonlinear characteristic. Basically, this controller consists of a feedback part and a feed-forward part. Whereas the feedback part is made up by a classical fuzzy controller, the feed-forward part is based on a fuzzy model taking into account the nonlinear...

Generalized Regression Neural Network (GRNN) is usually applied to the Function approximation. This paper, based on the principle of GRNN, presents a method for the predictive model of nonlinear complex system. The presented algorithm is applied to the learning and predicting process for the system modeling. The simulations show the described method has good effects on predicting the dynamic pr...