-------------------------------------------------------------------ABSTRACT---------------------------------------------------------------Prediction of rainfall for a region is of utmost importance for planning, design and management of irrigation and drainage systems. This can be achieved by different approaches such as deterministic, conceptual, stochastic and Artificial Neural Network (ANN)....

This paper investigates the potential application of artificial neural networks in permanent deformation parameter identification for rockfill dams. Two kinds of neural network models, multilayer feedforward network BP and radial basis function RBF networks, are adopted to identify the parameters of seismic permanent deformation for Zipingpu Dam in China. The dynamic analysis is carried out by ...

The modeling process is to find a parametric model whose dynamic behavior close to that process. This model will be used to make predictions of the process output, or to simulate the process in a control system...etc. In this work we used RBF neural networks for modeling nonlinear systems. Generally the problem in neural networks is often to find a better structure. We propose in this work a me...

Sensor fault detection, isolation (FDI) and accommodation has been investigated along with detection of unknown faults for an automotive engine. Radial basis function (RBF) neural networks are used for fault diagnosis. The RBF network is trained off line with K-means and batch least squares (BLS) algorithms. No fault and fault data are simulated in Matlab for four different sensors e.g. throttl...

In this paper we present and evaluate a Radial-basis-function neural network detector for Distributed-Denial-of-Service (DDoS) attacks in public networks based on statistical features estimated in short-time window analysis of the incoming data packets. A small number of statistical descriptors were used to describe the DDoS attacks behaviour, and an accurate classification is achieved using th...

This document is an introduction to radial basis function RBF networks a type of arti cial neural network for application to problems of supervised learning e g regression classi cation and time series prediction It is now only available in PostScript an older and now unsupported hyper text ver sion may be available for a while longer The document was rst published in along with a package of Ma...

A new recursively self-constructing fractal learning algorithm for an artificial neural network is presented. The network architecture is based on a tree-like structure of Radial Basis Function (RBF) networks. This paper examines some of the problems with RBF nets, and suggests a possible solution. Neurons in RBF nets have local receptive fields. The new learning algorithm ‘grows’ new sub-netwo...

Neural networks are investigated for predicting the magnitude of the largest seismic event in the following month based on the analysis of eight mathematically computed parameters known as seismicity indicators. The indicators are selected based on the Gutenberg-Richter and characteristic earthquake magnitude distribution and also on the conclusions drawn by recent earthquake prediction studies...

A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not...

This paper presents a new method of fault detection and isolation (FDI) for polymer electrolyte membrane (PEM) fuel cell (FC) dynamic systems under an open-loop scheme. This method uses a radial basis function (RBF) neural network to perform fault identification, classification and isolation. The novelty is that the RBF model of independent mode is used to predict the future outputs of the FC s...