This paper deals with the application of Neural Networks to binary hypothesis tests based on multiple observations. The problem of detecting a desired signal in Additive-White-Gaussian-Noise is considered, assuming that the desired signal observations are also gaussian, independent and identically distributed random variables. The test statistic is then the squared magnitude of the observation ...

The key point in design of radial basis function networks is to specify the number and the locations of the centers. Several heuristic hybrid learning methods, which apply a clustering algorithm for locating the centers and subsequently a linear leastsquares method for the linear weights, have been previously suggested. These hybrid methods can be put into two groups, which will be called as in...

Face Biometrics is a science of automatically identifying individuals based on their unique facial features. The paper presents neural network classifier(Radial Basis Function Network) to detect frontal views of faces. The curvelet transform, Linear Discriminant Analysis(LDA) are used to extract features from facial images first, and Radial Basis Function Network(RBFN) is used to classify the f...

This paper describes the analysis of algorithms for the hidden layer construction of network and for learning of the Radial Basis Function neural Network (RBFN). We compared results obtained by using of learning algorithms LMS (Least Mean Square) and Gradient Algorithms (GA) and results are obtained by using of algorithms APC-III and K-means for hidden layer contruction of neural network. The p...