The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model
provides a powerful representation for time series analysis, modeling and prediction due to …

This paper presents a multiobjective evolutionary algorithm to optimize radial basis function
neural networks (RBFNNs) in order to approach target functions from a set of input-output …

Least Squares Support Vector Machines (LS-SVM) are the state of the art in kernel methods
for regression. These models have been successfully applied for time series modelling and …

In this paper we propose a novel incremental learning approach based on a hybrid fuzzy
neural net framework. A key feature of the approach is the adaptation of the fuzzy neural …

An optimal online learning algorithm of a wavelet neural network is proposed. The algorithm
provides not only the tuning of synaptic weights in real time, but also the tuning of dilation …

Prediction of a stock price movement becomes very difficult problem in finance because of
the presence of financial instability and crisis. The time series describing the movement of …

Time Series (TS) models are well-known techniques that help to predict the weather in a
certain time period. The traditional TS prediction models take more prediction time …

This paper presents the problem of optimizing a radial basis function neural network from
training examples as a multiobjective problem and proposes an evolutionary algorithm to …

This paper compares some mutation operators containing expert knowledge about the
problem of optimizing the parameters of a Radial Basis Function Neural Network. It is shown …

In this paper temporal processing of time series function has been done using radial basis
function network. Radial basis function network structure is actually static but it has been …