Prediction of blasting induced air-overpressure using a radial basis function network with an additional hidden layer

Improving Accuracy of DGPS Correction Prediction in Position Domain using Radial Basis Function Neural Network Trained by PSO Algorithm

Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...

An incremental local radial basis function network

Radial basis function network for prediction of hydrological time series

In this study, a network using radial basis functions as the mapping function in the evolutionary equation for prediction of time series is presented. A radial basis function network requires the determination of the number of centres of the radial basis functions, their receptive field widths, and the linear weights of the network output layer. Methods to estimate the widths of the receptive f...

Recurrent radial basis function network for time-series prediction

This paper proposes a Recurrent Radial Basis Function network (RRBFN) that can be applied to dynamic monitoring and prognosis. Based on the architecture of the conventional Radial Basis Function networks, the RRBFN have input looped neurons with sigmoid activation functions. These looped-neurons represent the dynamic memory of the RRBF, and the Gaussian neurons represent the static one. The dyn...

Recurrent Radial Basis Function Network for Failure Time Series Prediction

An adaptive software reliability prediction model using evolutionary connectionist approach based on Recurrent Radial Basis Function architecture is proposed. Based on the currently available software failure time data, Fuzzy Min-Max algorithm is used to globally optimize the number of the k Gaussian nodes. The corresponding optimized neural network architecture is iteratively and dynamically r...