This paper considers the application of radial basis function neural networks for antenna array systems. An overview of neural network-based direction of arrival estimation is presented, with different radial basis neural networks for signal detection stage. Following that approach, for the case where a large number of neurons can be used, a radial basis function neural network with exact solut...

Seven different Radial Basis Functions have been applied in a Feedforward Neural Network and tested on five different real or simulated multivariate modelling problems. A short theory of Radial Basis Functions is presented as well as the particular implementation of the Radial Basis Function Network (RBFN). The real world data modelling problems are; identifying the dynamic actuator characteris...

In this paper, a nonlinear offline model of the solid oxide fuel cell (SOFC) is built by using a radial basis function (RBF) neural network based n a genetic algorithm (GA). During the process of modeling, the GA aims to optimize the parameters of RBF neural networks and the optimum alues are regarded as the initial values of the RBF neural network parameters. Furthermore, we utilize the gradie...

Analysis and recognition of human facial expressions from images and video forms the basis for understanding image content at a higher semantic level. Expression recognition forms the core task of intelligent systems based on human–computer interaction (HCI). In this paper, we explore the use of Artificial Neural Networks in performing expression recognition. We analyze seven basic types of hum...

Function approximation, which finds the underlying relationship from a given finite input-output data is the fundamental problem in a vast majority of real world applications, such as prediction, pattern recognition, data mining and classification. Various methods have been developed to address this problem, where one of them is by using artificial neural networks. In this paper, the radial bas...

The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R(n) is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns. In this paper, taking into account the specific feature of classification problems, where the goal i...

Due to increase in industrial and anthropogenic activities, air pollution has been a serious environmental problem all over the world. It was found that harmful emission into the air is a symbol for environmental force that affects seriously man’s health, natural life and agriculture; thus leading to major loss of the nation’s economy. In this paper, the prediction of the surface ozone layer pr...

In this paper, a probabilistic neural network (PNN) based classifier is used to judge the static security of the power system. The proposed classifier classifies the security of the power system based on the voltage profile of each bus in reference to changes in the generation and load profile in the system. The probabilistic neural network is used and compared with the radial basis function ne...