Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.

Nonlinear excitations in ferromagnetic chains with nearest and next nearest neighbor exchange interactions

Weakly nonlinear excitations in one-dimensional isotropic Heisenberg ferromagnetic chains with nearest- and next-nearest-neighbor exchange interactions are considered. Based on the properties of modulational stability of corresponding linear spin waves, the existence regions of bright and dark magnetic solitons of the system are discussed in the whole Brillouin zone. The antidark soliton mode which is convex soliton super-imposed with a plane wave component is obtained near the zero-dispersion points of the spin wave frequency spectrum. The analytical results are checked by numerical simulations. [S0163;1829(98)01838-4].