Automatic extraction and identification of chart patterns towards financial forecast
Applied Soft Computing
Development of Design Strategy for RBF Neural Network with the Aid of Context-Based FCM
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
An effective two-stage neural network model and its application on flood loss prediction
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Design of context-FCM based RBF neural networks with the aid of data information granulation
Proceedings of the 2012 ACM Research in Applied Computation Symposium
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A radial basis function (RBF) neural network (NN) is proposed to develop a rainfall-runoff model for three-hour-ahead flood forecasting. For faster training speed, the RBF NN employs a hybrid two-stage learning scheme. During the first stage, unsupervised learning, fuzzy min-max clustering is introduced to determine the characteristics of the nonlinear RBFs. In the second stage, supervised learning, multivariate linear regression is used to determine the weights between the hidden and output layers. The rainfall-runoff relation can be considered as a linear combination of some nonlinear RBFs. Rainfall and runoff events of the Lanyoung River collected during typhoons are used to train, validate,and test the network. The results show that the RBF NN can be considered a suitable technique for predicting flood flow