Inpainting is a method for repairing damaged images or to remove unwanted parts of an image. While this process has been performed by professional artists in the past, today, the use of this technology is emerging in the medical area – especially in the medical imaging realm. In this study, the proposed inpainting method uses a radial basis function (RBF) interpolation technique. We first expla...

This paper introduces a novel method for human face recognition that employs a set of different kind of features from the face images with Radial Basis Function (RBF) neural network called the Hybrid N-Feature Neural Network (HNFNN) human face recognition system. The face image is projected in each appropriately selected transform methods in parallel. The output of the RBF classifiers are fused...

Pattern classiication may be viewed as an ill-posed, inverse problem to which the method of regularization be applied. In doing so, a proper theoretical framework is provided for the application of radial basis function (RBF) networks to pattern classiication, with strong links to the classical kernel regression estimator (KRE)-based classiiers that estimate the underlying posterior class densi...

This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three diffe...

We present a new computational method by extending the immersed boundary (IB) method with a geometric model based on parametric radial basis function (RBF) interpolation of the Lagrangian structures. Our specific motivation is the modeling of platelets in hemodynamic flows, although we anticipate that our method will be useful in other applications involving surface elasticity. The efficacy of ...

Standard particle swarm optimization (SPSO) algorithm was modified by escape strategy of the particle velocity, and an escape PSO (EPSO) was proposed to overcome the shortcomings of being trapped in local optima because of premature convergence. To enhance the performance of radial basis function (RBF) neural network, the EPSO is combined with RBF neural network to form a EPSON hybrid algorithm...

This project consists of two parts. The first part is a general review of the previous and current research on human face recognition, including initial motivation, approaches, major problems and solutions, etc. The second part propose a new method for learning of radial basis function (RBF) neural networks which is based on subtractive clustering algorithm(SCA) and its application to face reco...

This paper presents a new method for on-board fault diagnosis for the air-path of spark ignition (SI) engines. The method uses an adaptive radial basis function (RBF) neural network to classify pre-defined possible faults from engine measurements to report the type and size of the fault. The RBF fault classifier adapts its widths and weights to model the time-varying dynamics of the engine and ...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...