this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

The unscented Kalman filter (UKF) is adopted in the interacting multiple model (IMM) framework to deal with the system nonlinearity in navigation applications. The adaptive tuning system (ATS) is employed for assisting the unscented Kalman filter in the IMM framework, resulting in an interacting multiple model adaptive unscented Kalman filter (IMM-AUKF). Two models, a standard UKF and an adapti...

Parameter estimation is considered to be one of the greatest challenges in computational systems biology. Biological experiments can measure only a fraction of the kinetic parameters and the rest has to be estimated in silico. Recently parameter estimation problems have been addressed in the framework of control theory. One of the most successful and widely used methods in control theory for es...

Neuroblastoma is the most common cancer in infants and the fourth most common cancer in children. Aggressive cell growth and chemoresistance are notorious obstacles in neuroblastoma therapy. Exposure to the anticancer drug ellipticine inhibits efficiently growth of neuroblastoma cells and induces apoptosis in these cells. However, ellipticine induced resistance in these cells. The upregulation ...

This paper presents sensor data fusion using Unscented Kalman Filter (UKF) to implement high performance vestibulo-ocular reflex (VOR) based vision tracking system for mobile robots. Information from various sensors is required to be integrated using an efficient sensor fusion algorithm to achieve a continuous and robust vision tracking system. We use data from low cost accelerometer, gyroscope...

Abstract Aiming to improve the position and velocity precision of INS/GNSS system during GNSS outages, a novel that combines unscented Kalman filter (UKF) nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX-based module utilized predict measurement updates UKF outages. A new offline approach for selecting optimal NARX suggested tested. This based ...

PURPOSE Neuroblastomas frequently show expression of the epidermal growth factor receptor (EGFR) and may therefore be susceptible to EGFR-targeted therapies. Here, EGFR expression and functionality was investigated in parental chemosensitive neuroblastoma cell lines (UKF-NB-3, IMR-32, NLF, SH-SY5Y) and their cisplatin-resistant sublines (UKF-NB-3(r)CDDP(1000), IMR-32(r)CDDP(1000), NLF(r)CDDP(10...

BACKGROUND Magnitude of current of injury (COI) consequent to pacemaker lead fixation is recognized as a predictor of acute lead stability. It is unclear whether dynamic monitoring of COI after lead fixation provides additional information beyond a single assessment performed at the time of fixation. OBJECTIVES This study was aimed to test the hypothesis that the time course of COI is related...

In a series of recent studies a new approach for applying the Kalman filter to nonlinear system, referred to as Unscented Kalman filter (UKF), was proposed. In this contribution we apply the UKF to several speech processing problems, in which a model with unknown parameters is given to the measured signals. We show that the nonlinearity arises naturally in these problems. Preliminary simulation...

In this paper three different filtering methods, the Extended Kalman Filter (EKF), the Gauss-Hermite Filter (GHF), and the Unscented Kalman Filter (UKF), are compared for state-only and coupled state and parameter estimation when used with log state variables of a model of the immunologic response to the human immunodeficiency virus (HIV) in individuals. The filters are implemented to estimate ...