A quasi-linear ARX neural network model (QARXNN) is a nonlinear model built using neural networks (NN). It has a linear-ARX structure where NN is an embedded system to give the parameters for the regression vector. There are two contributions in this paper, 1) Hierarchical Algorithms is proposed for the training of QARXNN model, 2) an adaptive learning is implemented to update learning rate in ...

Mutations in the Aristaless-related homeobox (ARX) gene are found in a spectrum of epilepsy and X-linked intellectual disability disorders. During development Arx is expressed in pallial ventricular zone (VZ) progenitor cells where the excitatory projection neurons of the cortex are born. Arx(-/Y) mice were shown to have decreased proliferation in the cortical VZ resulting in smaller brains; ho...

The in vitro differentiation of human embryonic stem cells (hESCs) offers a model system to explore human development. Humans with mutations in the transcription factor Aristaless Related Homeobox (ARX) often suffer from the syndrome X-linked lissencephaly with ambiguous genitalia (XLAG), affecting many cell types including those of the pancreas. Indeed, XLAG pancreatic islets lack glucagon and...

Precise control of electro-hydraulic actuator (EHA) system has been an interesting subject due to its nonlinearities and uncertainties characteristics. Good control can be designed when precise model of the system is available. Linear ARX modelling has widely been applied and satisfying result has been obtained, through linearization process. The objective of this paper is to compare ARX model ...

Modeling human operator’s behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator’s model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as th...

An integrated modeling and robust model predictive control (MPC) approach is proposed for a class of nonlinear systems with unknown steady state. First, the nonlinear system is identified off-line by RBF-ARX model possessing linear ARX model structure and state-dependent Gaussian RBF neural network type coefficients. On the basis of the RBF-ARX model, a combination of a local linearization mode...

This paper considers modeling and control problems of the non-stationary nonlinear processes whose dynamics depends on the working point. A hybrid RBF-ARX model-based predictive control (MPC) strategy without resorting to on-line parameter estimation for this kind of processes is presented. The RBF-ARX model is composed of the RBF networks and a rather general form of ARX model, which is identi...

a new ARX-Laguerre representation is recently built to model the dynamics of complex processes [1, 2]. The ARX-Laguerre models have proven their ability to accurately suit the behavior of systems. In this work, the model is exploited to diagnose the system by detecting its defaults. In this paper we build a proportional observer based on the ARX-Laguerre model. Therefore, the designed observer ...