Estimate of sediment load is required in a wide spectrum of water resources engineering problems. The nonlinear nature of suspended sediment load series necessitates the utilization of nonlinear methods to simulate the suspended sediment load. In this study Artificial Neural Networks (ANNs) are employed to estimate daily suspended sediment load. Two different ANN algorithms, Multi Layer Perce...

This paper presents an implementation of Artificial Neural Networks (ANN) for acoustic fingerprints recognition, applied to the identification of marine vessels. In many cases, the vessel recognition process from an audible signal is performed by human operators, which could lead to failures in the identification process. Before entering the ANN classification process, the signal is filtered an...

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of e...

in order to comparing geostatistics and artificial neural networks (ann) methods in predicting soil salinity and clay content this experiment was conducted in sistan region land. 121 soil samples were taken at the depth of 0-30 cm within the grid of 750 750 m and soil ec and clay percent were determined. 105 samples were used for training and 16 samples were used for test in both models. diffe...

abstract accurate prediction of river flow is one of the most important factors in surface water recourses management especially during floods and drought periods. in fact deriving a proper method for flow forecasting is an important challenge in water resources management and engineering. although, during recent decades, some black box models based on artificial neural networks (ann), have bee...

Evolving Artificial Neural Networks (ANN) is a new method that, except of the training, was applied to the structure optimization problem. This method combines ideas from both the evolution and adaptive signal processing techniques. An ANN is considered as a layered array of non-linear systems (the neuron models), each producing on its output a local error. Each of these errors is minimized usi...

In this paper, we propose a general design of rectangular microstrip antenna with and without rectangular aperture over ground plane, based on artificial neural networks (ANN) in conjunction with spectral domain formulation. In the design procedure, syntheses ANN model is used as feed forward network to determine the resonant frequency and bandwidth. Analysis ANN model is used as the reversed o...