Neuro-fuzzy ellipsoid basis function multiple classifier for diagnosis of urinary tract infections
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Identification of urinary track infections using soft computing techniques
Journal of Computational Methods in Sciences and Engineering
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Nonlinear time series modeling and prediction using local variable weights RBF network
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Hi-index | 0.00 |
We propose a modified radial basis function (RBF) network in which the regression weights are used to replace the constant weights in the output layer. It is shown that the modified RBF network can reduce the number of hidden units significantly. A computationally efficient algorithm, known as the expectation-maximization (EM) algorithm, is used to estimate the parameters of the regression weights. A salient feature of this algorithm is that it decomposes a complicated multiparameter optimization problem into L separate small-scale optimization problems, where L is the number of hidden units. The superior performance of the modified RB network over the standard RBF network is illustrated by computer simulations