Study and application of PSO-RBFNN model to nonlinear time series forecasting for geotechnical engineering;

Determining the input time delay τ of a neural network for nonlinear time series prediction;

Inter-well connectivityanalysis based on nonlinear time series

In this paper we consider the non-linear time series model under the random environment,xt=(α0+α1｜xt-1｜rβ+…+αp｜xt-p｜rβ)1/rεt(zt) We discuss the geometric ergodicity of the iterative sequence defined by this model.

In signal processing, dynamic system indentification,etc, linear time series analysis becomes increasingly inadequate in precision when non-linear time series actually exists.

The projection pursuit learning network to approximate multiple dimensional non-linear time series and the algorithm are presented in this paper, which can approximate the modes of multiple dimensional non-linear time series at any accuracy.

The paper suggests the forecasting model about objects that have nonlinear time sequence by using neural network model, and verifies in through an example.

The complexity measure is an important dynamic index to describe nonlinear time sequence.

Correlation dimension is an important parameter to measure a nonlinear time sequence quantitatively,and it is widely used to analyze harmonic component of power system and biomedical signal.