simulation of groundwater fluctuations plays a crucial role in management of watersheds and water demand balancing. recently, wavelet analysis has been used widely in time series decomposition and coupling with neural networks for hydrological modeling. in this paper, the ability of the wavelet-dynamic artificial neural networks (w-ann) model was applied in forecasting one-month-ahead of ground...

In this paper, a modified learning algorithm for the multilayer neural network with the multi-valued neurons (MLMVN) is presented. The MLMVN, which is a member of complex-valued neural networks family, has already demonstrated a number of important advantages over other techniques. A modified learning algorithm for this network is based on the introduction of an acceleration step, performing by...

This research investigates the synchronization of distributed delayed discrete-time fractional-order complex-valued neural networks. The necessary conditions have been established for stability proposed networks using theory discrete fractional calculus, Laplace transform, and Mittag–Leffler functions. In order to guarantee global asymptotic stability, adequate criteria are determined Lyapunov’...

Most artificial neural networks have nodes that apply a simple static transfer function, such as a sigmoid or gaussian, to their accumulated inputs. This contrasts with biological neurons, whose transfer functions are dynamic and driven by a rich internal structure. Our artificial neural network approach, which we call state-enhanced neural networks, uses nodes with dynamic transfer functions b...