Analysis of output frequencies of nonlinear systems

In this paper, an algorithm is derived for the determination of the output frequency ranges of nonlinear systems, which extends previous results on the output frequencies of nonlinear systems to a more general situation. The new results are significant for the analysis of the output frequency response of a wide class of nonlinear systems

Parametric characteristic analysis for the output frequency response function of nonlinear volterra systems

The output frequency response function (OFRF) of nonlinear systems is a new concept, which defines an analytical relationship between the output spectrum and the parameters of nonlinear systems. In the present study, the parametric characteristics of the OFRF for nonlinear systems described by a polynomial form differential equation model are investigated based on the introduction of a novel coefficient extraction operator. Important theoretical results are established, which allow the explicit structure of the OFRF for this class of nonlinear systems to be readily determined, and reveal clearly how each of the model nonlinear parameters has its effect on the system output frequency response. Examples are provided to demonstrate how the theoretical results are used for the determination of the detailed structure of the OFRF. Simulation studies verify the effectiveness and illustrate the potential of these new results for the analysis and synthesis of nonlinear systems in the frequency domain

The parametric characteristics of frequency response functions for nonlinear systems

The characteristics of the frequency response functions of nonlinear systems can be revealed and analyzed through the analysis of the parametric characteristics of these functions. To achieve these objectives, a new operator is defined, and several fundamental and important results about the parametric characteristics of the frequency response functions of nonlinear systems are developed. These theoretical results provide a significant and novel insight into the frequency domain characteristics of nonlinear systems and circumvent a large amount of complicated integral and symbolic calculations which have previously been required to perform nonlinear system frequency domain analysis. Several new results for the analysis and synthesis of nonlinear systems are also developed. Examples are included to illustrate potential applications of the new results

Identification of Nonlinear Systems Using Radial Basis Function Neural Network

This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the KMeans clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function

Initialization Strategy for Nonlinear Systems

The Study Group was asked to provide some hints concerning a choice of initial values to be used for nonlinear algebraic systems. The group has considered the available options and outlined the pros and cons of various methods and provided some recommendations

Bell's Theorem and Nonlinear Systems

For all Einstein-Podolsky-Rosen-type experiments on deterministic systems the Bell inequality holds, unless non-local interactions exist between certain parts of the setup. Here we show that in nonlinear systems the Bell inequality can be violated by non-local effects that are arbitrarily weak. Then we show that the quantum result of the existing Einstein-Podolsky-Rosen-type experiments can be reproduced within deterministic models that include arbitrarily weak non-local effects.Comment: Accepted for publication in Europhysics Letters. 14 pages, no figures. In the Appendix (not included in the EPL version) the author says what he really thinks about the subjec

Balancing for unstable nonlinear systems

A previously obtained method of balancing for stable nonlinear systems is extended to unstable nonlinear systems. The similarity invariants obtained by the concept of LQG balancing for an unstable linear system can also be obtained by considering a past and future energy function of the system. By considering a past and future energy function for an unstable nonlinear system, the concept of these similarity invariants for linear systems is extended to nonlinear systems. Furthermore the relation of this balancing method with the previously obtained method of balancing the coprime factorization of an unstable nonlinear system is considered. Both methods are introduced with the aim of using it as a tool for model reductio

Nonlocal feedback in nonlinear systems

A shifted or misaligned feedback loop gives rise to a two-point nonlocality that is the spatial analog of a temporal delay. Important consequences of this nonlocal coupling have been found both in diffusive and in diffractive systems, and include convective instabilities, independent tuning of phase and group velocities, as well as amplification, chirping and even splitting of localized perturbations. Analytical predictions about these nonlocal systems as well as their spatio-temporal dynamics are discussed in one and two transverse dimensions and in presence of noise.Comment: 13 pages, to be published in EPJ