BACKGROUND For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that...

Rainfall prediction is one of the most challenging task faced by researchers over years. Many machine learning and AI based algorithms have been implemented on different datasets for better purposes, but there not a single solution which perfectly predicts rainfall. Accurate still remains question to researchers. We offer learning-based comparison evaluation rainfall models Kashmir province. Bo...

Freshwater supply is a major challenge in regions with limited water resources and extremely arid climatic conditions. The objective of this study to model the monthly demand Kuwait using nonlinear autoregressive exogenous input (NARX) neural network approach. country lacks conventional surface characterized by climate. In addition, it has one fastest growing populations. study, linear detrendi...

Abstract Nonlinear Auto-Regressive eXogenous input (NARX) models are a popular class of nonlinear dynamical models. Often polynomial basis expansion is used to describe the internal multivariate mapping (P-NARX). Resorting fixed functions convenient since it results in closed form solution estimation problem. The drawback, however, that predefined does not necessarily lead sparse representation...

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...

The PAM robot arm is belonged to highly nonlinear systems where perfect knowledge of their parameters is unattainable by conventional modeling techniques because of the timevarying inertia, hysteresis and other joint friction model uncertainties. To guarantee a good tracking performance, robust-adaptive control approaches combining conventional methods with new learning techniques are required....

The paper is devoted to development of control algorithms for nonlinear parametrically uncertain systems. Original system dynamics is approximated by a set of local NARX models combined by a special mixing rule. Algorithm for local models’ parameters estimation and structure adjustment has been developed. Proposed approach allows straightforward designing of the combined feedforward/feedback co...