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

swarm optimization (PSO) and radial basis function neural network (RBFNN) algorithms. These intelligent algorithms are …

, and the radial basis function (RBF) neural network. The EPSO algorithm is employed to optimize the weight coefficients in …

Catalonia from 2001 to 2012. We find that multi-layer perceptron and radial basis function models outperform Elman networks …, being the radial basis function architecture the one providing the best forecasts when no additional lags are incorporated … forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis …

Catalonia from 2001 to 2012. We find that multi-layer perceptron and radial basis function models outperform Elman networks …, being the radial basis function architecture the one providing the best forecasts when no additional lags are incorporated … forecasting. We test the forecasting accuracy of three different types of architectures: a multi-layer perceptron, a radial basis …

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

analyzed that the Radial basis function network and Exact Radial basis network give highest recognition accuracy while Elman …