The Dst index is a key parameter which characterises the disturbance of the geomagnetic field in magnetic storms. Modelling of the Dst index is thus very important for the analysis of the geomagnetic field. A data-based modelling approach, aimed at obtaining efficient models based on limited inputoutput observational data, provides a powerful tool for analysing and forecasting geomagnetic activ...

This article discusses the identification of nonlinear dynamic systems using multi-layer perceptrons (MLP). It focuses on both the structure uncertainty and parameter uncertainty which have been widely explored in the 10 literature of nonlinear system identification. The main contribution is that an integrated analytic framework is proposed for automated neural network structure selection, para...

BACKGROUND Advanced methods of signal analysis of the preictal and ictal activity dynamics characterizing absence epilepsy in humans with absences and in genetic animal models have revealed new and unknown electroencephalographic characteristics, that has led to new insights and theories. NEW METHOD Taking into account that some network associations can be considered as nonlinear, an adaptive...

in the present study, the onset of double diffusive reaction-convection in a uid layer with viscous fluid, heated and salted from below subject to chemical equilibrium on the boundaries, has been investigated. linear and nonlinear stability analysis have been performed. for linear analysis normal mode technique is used and for nonlinear analysis minimal representation of truncated fourier serie...

In this paper two common methods for nonlinear principal component analysis are compared. These two methods are Auto-associative Neural Network (AANN) and Kernel PCA (KPCA). The performance of these methods in sensor data validation are discussed, finally a methodology which takes advantage of both of these methods is presented. The result is a unique approach to nonlinear component mapping of ...

In this contribution we apply different approaches to solve four rather different MINLP problems: special extensions to time-indexed formulations of production planning problems, a production planning problem in BASF’s petrochemical division, a site analysis of one of BASF’s bigger sites, and a process design problem. The first problem is related to a useful nonlinear extension of production pl...

The representation of hidden variable models by attractor neural net works is studied Memories are stored in a dynamical attractor that is a continuous manifold of xed points as illustrated by linear and nonlinear networks with hidden neurons Pattern analysis and synthesis are forms of pattern completion by recall of a stored memory Analysis and synthesis in the linear network are performed by ...

In this paper, we present a new predictive hybrid model using discrete wavelet transform (DWT), and the artificial neural network (ANN) to reduce the bullwhip effect of demand in supply chain to obtain a real amount of final customer demand. Also, we compare our result with more comprehensive sample of previous research to extend the scope of our study. In this new research our methodology is c...

The relationship between the topology and collective function of a nonlinear oscillator network was investigated using nonlinear electrochemical oscillators. The constitutive experiments showed that the physiological robustness in the living system is due to their topological redundancy and asymmetry in the nonlinear network. key words: nonlinear oscillator, complex network, physiological funct...