The problem of modulation instability of a self-focused beam in a homogeneous nonlinear medium with saturation and anomalous group-velocity dispersion is solved numerically. It is shown that the results of this instability is beam breakup into a periodic train of three-dimensional (3D) spatial solitary waves. It is also shown that other types of periodic initial conditions can produce a periodi...

Traditional linear spectral analysis techniques of the vibration signals, based on auto-power spectrum, are used as common tools of rotating components diagnoses. Unfortunately, linear spectral analysis techniques are of limited value when various spectral components interact with one another due to nonlinear or parametric process. In such a case, higher order spectral (HOS) techniques are reco...

B-spline neural network (BSNN), a type of basis function neural network, is trained by gradient-based methods, which may fall into local minimum during the learning procedure. To overcome the problems encountered by the conventional learning methods, differential evolution (DE)  an evolutionary computation methodology  can provide a stochastic search to adjust the control points of a BSNN are...

| In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, we have interval neural networks. By analyzing the mathematical model, we categorize general three-layer neural network training problems into two types. One of them can be solved by ̄nding numerical solutions of non...

The background scene generator MATISSE, whose main functionality is to generate natural background radiance images, makes use of the so-called Correlated K (CK) model. It necessitates either loading or computing thousands of CK coefficients for each atmospheric profile. When the CK coefficients cannot be loaded, the computation time becomes prohibitive. The idea developed in this paper is to su...

There is currently considerable interest in developing general nonlinear density models based on latent, or hidden, variables. Such models have the ability to discover the presence of a relatively small number of underlying 'causes' which, acting in combination, give rise to the apparent complexity of the observed data set. Unfortunately, to train such models generally requires large computatio...

in this study, the nonlinear optical properties and optical limiting performance of the silver nanoparticles (agnps) in distilled water are investigated. the nonlinear absorption coefficient of the colloid is measured by the z-scan technique. the optical limiting behavior of the agnp suspension is investigated under exposure to nanosecond laser pulses at 532 nm. the results show that nonlinear ...

This paper describes a technique to separate the speech of two speakers recorded over a single channel. The main focus of this research is to separate overlapping voiced speech signals using constrained nonlinear optimization. Based on the assumption that voiced speech can be modeled as a slowly-varying vocal tract filter with a quasi-periodic train of impulses, the speech waveform is represent...

An online fault detection and isolation scheme for nonlinear systems based on neurofuzzy modelling and pattern matching is developed in this paper. The system is first modelled offline by a neurofuzzy network using data obtained under normal operating conditions. Another neurofuzzy network is then used to model the residual, which is the difference between the output of the system and that from...

The conventional fuzzy CMAC can be viewed as a basis function network with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However,it requires an enormous memory and the dimension increase exponentially with the input number. Hierarchical fuzzy CMAC (HFCMAC) can use less memory to model nonline...