This article describes a new structure to create a RBF neural network; this new structure has 4 main characteristics: firstly, the special RBF network architecture uses regression weights to replace the constant weights normally used. These regression weights are assumed to be functions of input variables. The second characteristic is the normalization of the activation of the hidden neurons (w...

We review the use of feed-forward networks as estimators of probability densities in hidden Markov modelling. In this paper we are mostly concerned with radial basis functions (RBF) networks. We note the isomorphism of RBF networks to tied mixture density estimators; additionally we note that RBF networks are trained to estimate posteriors rather than the likelihoods estimated by tied mixture d...

In this paper we show that radial distortion of images invalidates the geometric constraint on which self-calibration of a rotating camera is based — that 3D lines drawn between matched features all intersect at the rotation centre. We develop a geometric picture showing how radial distortion violates this constraint and discuss the implications for self-calibration of a rotating camera. In par...

In this paper, two theorems are proved, one for existence of the operator L (f ; x, y, λ) and the others for its pointwise convergence to f (x 0 , y 0) , as (x, y, λ) tends to (x 0 , y 0 , λ 0). In contrast to previous works, the kernel function is radial.

Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two diierent cost functions for Support Vectors: training with (i) an insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models. Two applications are considered: data from (a) a noisy (normal and uniform n...

RBF approximations would appear to be very attractive for approximating spatial derivatives in numerical simulations of PDEs. RBFs allow arbitrarily scattered data, generalize easily to several space dimensions, and can be spectrally accurate. However, accuracy degradations near boundaries in many cases severely limit the utility of this approach. With that as motivation, this study aims at gai...

This paper aims in comparing countries with different energy strategies, and demonstrate the close connection between environment and economic growth in the ex-Eastern countries, during their transition to market economies. We have developed a radial-basis function neural network system, which is trained to classify countries based on their emissions of carbon, sulphur and nitrogen oxides, and ...

Detection of rain/no-rain condition on the ground is an important for application of radar rainfall algorithms. A radial basis function (RBF) neural network-based scheme for rain/no-rain determination on the ground using vertical profiles of radar data is described in this paper. Evaluation based on WSR-88D radar over central Florida indicates that rain/no-rain condition can be inferred fairly ...

We propose a method for optimizing the complexity of Radial basis function (RBF) networks. The method involves two procedures: adaptation (training) and selection. The first procedure adaptively changes the locations and the width of the basis functions and trains the linear weights. The selection procedure performs the elimination of the redundant basis functions using an objective function ba...