The aristaless-related homeobox (ARX) gene has been implicated in a wide spectrum of disorders ranging from phenotypes with severe neuronal migration defects, such as lissencephaly, to mild forms of X-linked mental retardation without apparent brain abnormalities. To better understand its role in corticogenesis, we used in utero electroporation to knock down or overexpress ARX. We show here tha...

Due to the nonlinear feature of a ozone process, regression based models such as the autoregressive models with an exogenous vector process (ARX) suffer from persistent diurnal behaviors in residuals that cause systematic over-predictions and under-predictions and fail to make accurate multi-step forecasts. In this article we present a simple class of the functional coefficient ARX (FARX) model...

A pneumatic actuator is highly nonlinear, which makes the precise position control of this difficult to achieve. In order achieve control, selecting a suitable model structure prerequisite before estimation. This selection based upon an understanding physical systems. paper, black-box chosen as system identification for modeling Intelligent Pneumatic Actuator (IPA) and variety parametric struct...

Mutations in the Aristaless Related Homeobox (ARX) gene are associated with a spectrum of structural (lissencephaly) and functional (epilepsy and intellectual disabilities) neurodevelopmental disorders. How mutations in this single transcription factor can result in such a broad range of phenotypes remains poorly understood. We hypothesized that ARX functions through distinct interactions with ...

Recursive non-linear Bayesian estimation is addressed using equivalence approach as motivating framework. Its specific form – tailored to a model class covering non-normal ARX (auto-regression with exogenous variables) models, models with discrete outputs and continuous-valued regression vectors and their dynamic mixtures – is presented. The resulting algorithms provide efficient solutions of d...

Problem of parameterization of multi-output autoregressive regressive Gaussian model (ARX) is studied in the context of prior design of adaptive controllers. The substantial role of prior distribution of unknown parameters on the parameterization is demonstrated. Among several parameterizations a nontraditional one is advocated which • makes it possible to model the system output entry-wise, th...

This paper considers an exploratory modeling strategy applied to a large scale reallife problem of power load forecasting. Different model structures are considered, including Autoregressive models with eXogenous inputs (ARX), Nonlinear Autoregressive models with eXogenous inputs (NARX), both of which are also extended to incorporate residuals that follow an Autoregressive (AR) process (AR-(N)A...

2209 Fig. 1. Bode plot of the true system and mean errors. The reference signal r and the noise e 0 were chosen as independent, zero mean, Gaussian white noise signals, with variances 1 (=8r(!)) and 0.01 (= 0), respectively. A Monte Carlo simulation consisting of 1024 different runs was performed. In each run we generated N = 1024 data points and identified the system directly using second-orde...

This paper presents a systems identification method, for discrete time linear systems, based on an evolutionary approach, which allows achieving the selection of a suitable structure and the parameters estimation, using non conventional objective functions. This algorithm incorporates parametric crossover and parametric mutation along a weighted gradient direction [1]. The performance of the pr...