abstract in this research, a radial basis function artificial neural network (rbf-ann) model was developed to predict the hot deformation flow curves of api x65 pipeline steel. the results of the developed model was compared with the results of a new phenomenological model that has recently been developed based on a power function of zener-hollomon parameter and a third order polynomial functio...

A complex valued neural network is a neural network, which consists of complex valued input and/or weights and/or thresh olds and/or activation functions. Complex-valued neural networks have been widening the scope of applications not only in electronics and informatics, but also in social systems. One of the most important applications of the complex valued neural network is in image and visio...

The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: 1) the necessary and sufficient condition for a function of one variable to be qualified as an activation function in RBF network is that the function is not an even polynomial, and 2) the capability of approximation to nonlinear functionals and operators b...

This analysis principally focuses on the implementation of Radial basis function (RBF) and back propagation (BPA) algorithms for training artificial neural network (ANN) to get the optimum mixture of Hydro fluorocarbon (HFC) and organic compound (Hydrocarbons) for obtaining higher coefficient of Performances (COPs). The thermodynamical properties of mixed refrigerants are observed using REFPROP...

In this paper, a novel controller is proposed for discrete-time nonlinear systems with uncertain output-channel time delays using RBF neural networks and information entropy. The controller is designed by minimizing the quadratic Renyi entropy. The probability density function (PDF) of tracking error is estimated by Parzen windowing technique. The Jacobian information is estimated by an RBF neu...

Identification and classification of differentially expressed genes is a challenging process.The study was performed to find applicability of supervised feed forward neural networks to solve classification problems in microarray data.We used two neural network learning algorithms namely RBF and MLP for the classification of gene expression of mycobacterium tuberculosis.The result showed that ML...

A new adaptive multiple-controller is proposed incorporating a neural network based Generalized Learning Model (GLM). The GLM assumes that the unknown complex plant is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a Radial Basis Function (RBF) neural-network based learning sub-model . The proposed non-linear multiple-controller methodology prov...

This paper presents a high accuracy human face recognition system using multi-feature extractors and multi-stages classifiers (MFMC), which are fused together through fuzzy integral. The classifiers used in this paper are Radial Basis Function (RBF) neural network while feature vectors are generated by applying PZM, PCA and DCT to the face images separately. Each of the feature vectors are sent...

With the evolvement of fMRI’s, a great amount of attention has been given to classifying cognitive states of human beings. Several machine learning approaches have been used to train single-subject classifiers to do so. We present a different method using a neural network and a RBF SVM to train one classifier across all subjects. For the single-subject classifier case, we experiment with PCA as...

Abstract. Aim at the limitation of traditional PID controller has certain limitation, the traditional PID control is often difficult to obtain satisfactory control performance, and the RBF neural network is difficult to meet the requirement of real-time control system. To overcome it, an adaptive PID control strategy based on (RBF) neural network is proposed in this paper. The results show that...