This paper presents a novel approach in learning algorithms commonly used for training radial basis function (RBF) neural networks. This approach could be used in applications that need real-time capabilities for retraining RBF neural networks. The proposed method is a Three-Phase Learning Algorithm that optimizes the functionality of the Optimum Steepest Decent (OSD) learning method. RBF neura...

The acoustic emission (AE) technology can be used to assess the security condition of oil storage tank without opening pot. Signal recognition is a foundation to analyze the corrosion status for oil storage tanks. Because of inadequateness of the analysis method of parameters, a new acoustic emission signal recognition method is proposed based on wavelet transform and RBF neural network. AE sig...

A parametric study was carried out in order to investigate the buckling capacity of the vertically stiffened cylindrical shells. To this end ANSYS software was used. Cylindrical steel shells with different yield stresses, diameter-to-thickness ratios (D/t) and number of stiffeners were modeled and their buckling capacities were calculated by displacement control nonlinear static analysis.  Radi...

This paper presents experiments using an adaptive learning compo nent based on Radial Basis Function RBF networks to tackle the unconstrained face recognition problem using low resolution video in formation Firstly we performed preprocessing of face images to mimic the e ects of receptive eld functions found at various stages of the hu man vision system These were then used as input representat...

In this paper, after analyzing the reasons of poor generalization and overfitting in neural networks, we consider some noise data as a singular value of a continuous function jump discontinuity point. The continuous part can be approximated with the simplest neural networks, which have good generalization performance and optimal network architecture, by traditional algorithms such as constructi...

Abstract: Much attention has been paid to Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning. A Taylor-series expansion location algorithm based on the RBF neural network (RBF-TSE) is proposed before to the performance of TSE highly depends on the initial estimation. In order to ha...

In this paper proposed a hybrid model for feature selection and intrusion detection. Feature selection is important issue in intrusion detection. The selection of feature in attack attribute and normal traffic attribute is challenging task. The selection of known and unknown attack is also faced a problem of classification. PCNN is dynamic network used for the process of feature selection in cl...

Conventionally, in radial basis function (RBF) network width factor is constructed by obtaining r-nearest neighbor rule or taking equal to a constant for all Gaussian functions. This paper proposes an approach for the construction of width factor using genetic algorithm to optimize the Gaussian function. Our experimental results show that our proposed optimal-based width outperforms the convent...

An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the...