This paper describes a method to improve the robustness of a face recognition system based on the combination of two compensating classifiers. The face images are preprocessed by the appearance-based statistical approaches such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). LDA features of the face image are taken as the input of the Radial Basis Function Network ...

This report takes advantage of Neural Networks (NN) and Fuzzy Inference Systems (FIS) in order to design a system suited to predict time series. We choose the solution of the Mackey{Glass time delay diierential equation in the chaotic domain as a sample problem. Fuzzy rules are generated from the sample data. The system performance is improved by means of Evolution Strategy (ES). The rules of t...

This paper suggests a model for classifying skin lesions into benign and malignant melanoma using radial basis function network (RBFN). The model initially converts the color image into gray image and then applies Median filter for removing thin hairs and other noises. It then segments the cancerous region through segmentation and then extracts features that represent the characteristics of the...

Thermography is a non-invasive and non-contact imaging technique widely used in the medical arena. This paper investigates the analysis of thermograms with the use of biostatistical methods and Artificial Neural Networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established. The proposed advanced technique is a multipronge...

In this paper it is proposed an image compression method based on the idea of fitting a set of Neural Networks (NNs) outputs to the image surface, which is a three-dimensional surface where the pixel values are considered as heights (z-values) defined on the x–y ground plane. An image is divided into subimages (blocks) using a quad tree, according to the complexity of the image surface. Individ...

Wind speed forecast is essential in wind energy conversion system and may fail to operate power plant at non optimal region if not properly forecasted. This paper focuses the short term wind speed forecasting using conventional statistical method and artificial neural networks such as back propagation network (BPN), generalized regression neural network (GRNN) and radial basis function networks...

The orthogonal least squares algorithm (OLS) and the support vector regression (SVR) are two popular approaches to choose the structure of the Radial Basis Function Network (RBFN). The former is derived based only on the modelling errors, whilst the latter also on the model complexity. A comparison of the generalization results of networks selected from the OLS and the SVR is presented here usi...

| Adaptive learning dynamics of the Radial Basis Function Network (RBFN) are compared with a scale-based clustering technique Won93] and a relationship between the two is pointed out. Using this link, it is shown how scale-based clustering can be done using the RBFN, with the Radial Basis Function (RBF) width as the scale parameter. The technique suggests the \right" scale at which the given da...

In this paper, a novel learning strategy for radial basis function networks (RBFN) is proposed. By adjusting the parameters of the hidden layer, including the RBF centers and widths, the weights of the output layer are adapted by local optimization methods. A new local optimization algorithm based on a combination of the gradient and Newtonmethods is introduced. The efficiency of some local opt...

Total nitrogen has been determined by using a model developed between the conventional chemical measurements and diffuse reflectance spectra in the near-infrared region. Samples (244) from different types of soils with total nitrogen contents ranging from 0.20 to 13.60% (m/m) were modeled by partial least-squares regression (PLS), multi-layer perceptron feed-forward networks (MLP) and radial ba...