This paper is intender to be a simple example illustrating some of the capabilities of Radial basis function by pruning with QLP decomposition. The applicability of the radial basis function (RBF) type function of artificial neural networks (ANNS) approach for re-estimate the Box, Traingle, Epanechnikov and Normal densities. We propose an application of QLP decomposition model to reduce to the ...

Numerical models of glacier and ice sheet dynamics traditionally employ nite difference or nite element methods. Although these are highly developed and mature methods, they su er from some drawbacks, such as inability to handle complex geometries ( nite di erences) or a costly assembly procedure for nonlinear problems ( nite elements). Additionally, they are mesh-based, and therefore moving do...

In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO2, SiO2, and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters ...

In this paper, an intelligent model-based fault detection (FD) is developed for proton exchange membrane fuel cell (PEMFC) dynamic systems using an independent radial basis function (RBF) networks. The novelty is that this RBF networks is used to model the PEMFC dynamic systems and residuals are generated based on the differences between the PEMFC systems and RBF networks model. Later, based on...

Glomerular filtration rate (GFR) and renal blood flow (RBF) are normally kept constant via renal autoregulation. However, early diabetes results in increased GFR and the potential mechanisms are debated. Tubuloglomerular feedback (TGF) inactivation, with concomitantly increased RBF, is proposed but challenged by the finding of glomerular hyperfiltration in diabetic adenosine A(1) receptor-defic...

This paper presents an intrusion detection system of hybrid neural network model based on RBF and Elman. It is used for anomaly detection and misuse detection. This model has the memory function .It can detect discrete and related aggressive behavior effectively. RBF network is a real-time pattern classifier, and Elman network achieves the memory ability for former event. Based on the hybrid mo...

ANNARIMA that combines both autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model is a valuable tool for modeling and forecasting nonlinear time series, yet the over-fitting problem is more likely to occur in neural network models. This paper provides a hybrid methodology that combines both radial basis function (RBF) neural network and auto regression...