A comparative study of the application of Gaussian Mixture Model (GMM) and Radial Basis Function (RBF) in biometric recognition of voice has been carried out and presented. The application of machine learning techniques to biometric authentication and recognition problems has gained a widespread acceptance. In this research, a GMM model was trained, using Expectation Maximization (EM) algorithm...

In this paper we propose the idea of building a new software reliability models using Radial Basis Function (RBF) network. The RBF network is easy to design and the network structure can be represented in a simple mathematical equation. Our goal is to build a generalized model that can be used for software predication [1]. The RBF network was trained with a set of data collected from the testin...

We present a forecasting strategy based on stationary wavelet transform combined with radial basis function (RBF) neural network to improve the accuracy of 3-month-ahead hake catches forecasting of the fisheries industry in the central southern Chile. The general idea of the proposed forecasting model is to decompose the raw data set into an annual cycle component and an inter-annual component ...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

A linear autoregressive/moving-average model was developed to describe the dynamic relationship between mean arterial pressure (MAP), renal sympathetic nerve activity (SNA) and renal blood flow (RBF) in conscious rabbits. The RBF and SNA to the same kidney were measured under resting conditions in a group of eight rabbits. Spectral analysis of the data sampled at 0.4 Hz showed that the low-pass...

It has limitations to apply the traditional mathematical model to assess the risk of the information security for it is characterized by its nonlinearity and uncertainty. The RBF Neural Networks Theory, Particle Swarm Optimization (PSO) Analysis and Fuzzy Evaluation are combined to build a particle swarm optimizing model of Information Security Risk Assessment based on RBF Neural Networks, so a...

In this paper, we price American options using the radial basis function (RBF) interpolation method. Two processes for the volatility are assumed: local volatility and stochastic volatility. In particular, we focus on the constant elasticity of variance (CEV) model (Cox and Ross (1976)) and the Heston model (Heston (1993)). Several experiments are performed to evaluate the pricing accuracy and ...

In order to guarantee security and stable operation of electric vehicle, it is necessary to on-line estimation for the state of charge (SOC) of batteries. The power battery is a complex nonlinear system, and Radial Basis Function Neural Network (RBF NN) has advantages in solving nonlinear problems, so the model of on-line SOC estimation based on RBF NN is proposed. In order to improve the predi...

This paper proposes the use of radial basis function (RBF) networks in the modelling of non-linear engine processes. A pertinent application of such a model is the reconstruction of cylinder pressure based upon the instantaneous angular velocity of the engine crankshaft. Distinction is made between parametric and non-parametric models and applications to which each is suited. The structure of a...

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...