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...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

There is considerable neurobiological evidence suggesting that schizophrenia is associated with reduced corticocortical connectivity. The authors describe two neural network computer simulations that explore functional consequences of these abnormalities. The first utilized an "attractor" neural network capable of content-addressable memory. Application of a pruning rule that eliminated weaker ...

The aim of the paper is to investigate influence thalamo-cortical connectivity on conscious perception visual stimuli. We conducted simulation experiments changing key parameters our spike timing neural network model and decision making that are supposed be related perception, namely bottom-up top-down connections between thalamic relay, including Thalamic reticulate nucleus (TRN) Lateral genic...

When an ambiguous stimulus is observed, our perception undergoes dynamical changes between two states, a situation extensively explored in association with the Necker cube. Such phenomenon refers to bistable perception. Here, we present a model neural network composed of forced FitzHugh-Nagumo neurons, implemented also experimentally in an electronic circuit. We show, that under a particular co...