Due to the advances of network technologies and multimedia communications, Quality of Service (QoS) becomes an increasingly important issue in network communications. Many traditional assessment techniques were designed to evaluate the QoS of multimedia applications transmitted over these networks. In this paper, a new QoS evaluation system has been developed. The proposed system is based on us...

Great effect of three way catalytic convertor (TWC) performance on oxygen sensor output voltage has made the sensor (located after catalyst) as the main signal in almost all today’s TWC monitoring algorithms. In this paper output voltage of nonlinear oxygen sensor is estimated using a nonlinear autoregressive with exogenous inputs (NARX) model. The estimation uses ECU calculated exhaust gas flo...

This study examines modeling and simulation of the transient thermal behavior of a solar collector adsorber tube. The data used for model setup and validation were taken experimentally during the start-up procedure of a solar collector adsorber tube. ANN models are developed based on the nonlinear autoregressive with exogenous input NARX model and are implemented using the MATLAB® tools includi...

This paper contains a nonlinear, nonstationary autoregressive model whose intercept changes deterministically over time. The intercept is a flexible function of time, and its construction bears some resemblance to neural network models. A modelling technique, modified from one for single hidden-layer neural network models, is developed for specification and estimation of the model. Its performa...

1. Partially-observed Markov processes 1 2. A first example: a discrete-time bivariate autoregressive process. 3 3. Defining a partially observed Markov process in pomp. 3 4. Simulating the model 5 5. Computing likelihood using particle filtering 6 6. Interlude: utility functions for extracting and changing pieces of a pomp object 9 7. Estimating parameters using iterated filtering: mif 10 8. N...

In this paper a fuzzy autoregressive (AR) model described in [1] is used to model and predict highspeed network traffic. This model approximates a complex nonlinear time-variant process by combining linear local autoregressive processes using a fuzzy clustering algorithm. We propose a method to estimate the traffic effective bandwidth at regular intervals, assuming the network traffic can be de...

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin-Huxley (H-H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The ...

This paper deals with the identification of MIMO cement mill process using Non-linear Autoregressive with Exogenous Inputs (NARX) models with wavelet network. NARX identification, based on a sequence of input/output samples, collected from a real cement mill process is used for black-box modeling of non-linear cement mill process. The NARX model is considered for two inputs and two outputs of s...

This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive recurrent network proposed is based on the nonlinear autoregressive with exogenous input model (NARX) and works according to a multi-step (MS)...

In this paper, we introduce an autoregressive model which has an evolution that is driven by an exogenous pilot signal. This model shares some properties with TAR (Threshold Auto Regressive) models and STAR (Smooth Transition Auto Regressive) models. This text de nes the model, it presents an estimator for this model, and an estimator for the variance of the innovation, which is not constant in...