عنوان مقاله [English]
In artificial neural networks (ANN), the available methods of neural learning and calibration is according to multi-layer structure of perceptron, but these methods have some problems results from lack of convergence in learning methods, lack of stability in network weights in conditions that there is great criteria deviation in input data spectrum and finally need for much data and information for network learning. A new compound method of artificial neural network-non linear mathematical optimum was introduced in this research to overcome this problem and artificial neural network designed by using error back propagation method was introduced as a strong device for estimating the rate of sediment in the reservoir of Ecbatan dam. According to that, the designed model by various knots in input and hidden layer was performed by using the equation of sediment discharge and water current and statistics of Yalfan station at Abshineh river. Calibration results showed that 6 knots at input layer and 8 knot at hidden layer should be used to distribute sediment in reservoir of Ecbatan dam.