In this paper, nonlinear dynamical black-box models of a common rail injection system for a CNG engine are developed. In particular, the common rail pressure dynamics is modeled on the basis of three input signals, easily and cheaply measurable on board a vehicle. The nonlinear model is identified by means of Multi Layer Perceptron neural networks. Both non-autoregressive (NMAX) and autoregress...

Modelling and forecasting Stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. This nonlinearity affects the efficiency of the price characteristics. Using an Artificial Neural Network (ANN) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

An important aspect of real time glucose control in diabetic subjects is forecasting the future glucose profile. From a modeling perspective, the complexity of interactions between plasma glucose and its controlling hormone insulin, demands a nonlinear modeling approach for representing the dynamics of glucose metabolism. In this paper, a technique based on the hybrid approach of nonlinear auto...

Within the last years several methods for the analysis of nonlinear autoregressive time series have been proposed. As in linear autoregressive models main problems are model identification, estimation and prediction. A boosting method is proposed that performs model identification and estimation simultaneously within the framework of nonlinear autoregressive time series. The method allows to se...

The use of both linear autoregressive model coefficients and nonlinear measures for classification of EEG signals recorded from healthy subjects and epilepsy patients is investigated. A total of seven nonlinear measures namely the approximate entropy, largest lyapunov exponent, correlation dimension, nonlinear prediction error, hurst exponent, third order autocovariance, asymmetry due to time r...

Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes...

This study examines whether the Taiwan’s public debt is sustainable utilizing an unrestricted two-regime threshold autoregressive (TAR) model with an autoregressive unit root. The empirical results show that Taiwan’s public debt appears as a nonlinear series and is stationary in regime 1 but not in regime 2. This result implies that while Taiwan’s public debt was mostly sustainable over the 199...

A nonlinear generalization of the family of autoregressive signal models is introduced. This generalization can be viewed as an autoregressive model with state-varying parameters. For such signals, minimum mean-square error prediction can be reformulated as an interpolation problem. A novel interpretation of the signal as a codebook for its own prediction leads to an interpolation strategy rese...