dataset for the radial basis function neural network model to forecast the option price. However, the sample size of option … function neural network model and a bootstrap method. In the proposed model, the dynamic n-order 2-factor fuzzy time series … series neural network with bootstrap model, is composed of a dynamic n-order 2-factor fuzzy time series model, a radial basis …

function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm … (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis …

autoregressive (AR) model but also achieves better results than a radial basis function neural network (RBFNN)-based model in terms …

paper presents a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural … network), and DEKF (dual extended Kalamn filter) to construct a prediction model (SVR–DEKF–RBFNN) for short-term load …

comparison of three different artificial intelligence methods, namely support vector machines (SVMs), radial basis function … neural network (RBF-NN) and multilayer perceptrons (MLPs); in addition to subjecting the explanatory variables to principal …

experiments, and Taguchi Orthogonal Array (OA). Then, metamodel is constructed by Radial Basis Function Neural Network (RBFNN) to …