The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniq...

This paper proposes a non-recurrent training algorithm, resilient propagation, for the Simultaneous Recurrent Neural network operating in relaxation-mode for computing high quality solutions of static optimization problems. Implementation details related to adaptation of the recurrent neural network weights through the non-recurrent training algorithm, resilient backpropagation, are formulated ...

drought is random and nonlinear phenomenon and using linear stochastic models, nonlinear artificial neural network and hybrid models is advantaged for drought forecasting. this paper presents the performances of autoregressive integrated moving average (arima), direct multi-step neural network (dmsnn), recursive multi-step neural network (rmsnn), hybrid stochastic neural network of directive ap...

the safety of buried pipes under repeated load has been a challenging task in geotechnical engineering. in this paper artificial neural network and regression model for predicting the vertical deformation of high-density polyethylene (hdpe), small diameter flexible pipes buried in reinforced trenches, which were subjected to repeated loadings to simulate the heavy vehicle loads, are proposed. t...

with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

The neuron state modeling and the local field modeling provides two fundamental modeling approaches to neural network research, based on which a neural network system can be called either as a static neural network model or as a local field neural network model. These two models are theoretically compared in terms of their trajectory transformation property, equilibrium correspondence property,...